Show of Hands:Votes: Party classification:: Q109244NA_AllX_cnk01_fit.models_1
bdanalytics
Date: (Mon) Jun 13, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
'(glbObsAll[, "Q109244"] != "")' # NA
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(2, 3, 4, 5, 6, 7, 8, 16, 32, 64, 128, 247) # accuracy(5) = 0.6154
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
# glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
glbMdlFamilies[["All.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
# AllX__rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "0.05 0.06367626 0.07 0.08 0.09167068")
# ) # max.Accuracy.OOB = 0.6020202 @ 0.55 0.03
# glbMdlTuneParams <- rbind(glbMdlTuneParams
# ,cbind(data.frame(mdlId = "All.X##rcv#glmnet"), AllX__rcv_glmnetTuneParams)
# )
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "degree", vals = "1")
# ,data.frame(parameter = "nprune", vals = "256")
# )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
# bagEarthTuneParams))
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list(NULL# NULL # : default
# ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# # c(NULL)))
# c("zv.pca.spatialSign")))
# ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
# "nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
"max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
"min.elapsedtime.everything",
# "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # NULL : default #"auto"
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
# , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
, "LGOCV"
, "adaptive_cv" # crashed for Q109244No
# , "adaptive_boot" #error: adaptive$min should be less than 3
# , "adaptive_LGOCV" #error: adaptive$min should be less than 3
)
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- # NULL #: default
c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv")
# c("Votes_Ensemble_cnk06_out_fin.csv")
glbOut <- list(pfx = "Q109244NA_AllX_cnk01_fit.models_1_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- NULL # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- "fit.models_1" # default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- NULL #"data/Q109244No_AllXNOr_cnk01_fit.models_1_fit.models_1.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 7.843 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/train2016.csv..."
## [1] "dimensions of data in ./data/train2016.csv: 5,568 rows x 108 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 1 1938 Male Married (w/kids)
## 2 4 1970 Female over $150,000 Domestic Partners (w/kids)
## 3 5 1997 Male $75,000 - $100,000 Single (no kids)
## 4 8 1983 Male $100,001 - $150,000 Married (w/kids)
## 5 9 1984 Female $50,000 - $74,999 Married (w/kids)
## 6 10 1997 Female over $150,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621 Q122769
## 1 Democrat No No No No
## 2 Bachelor's Degree Democrat Yes No No No
## 3 High School Diploma Republican Yes Yes No
## 4 Bachelor's Degree Democrat No Yes No Yes No
## 5 High School Diploma Republican No Yes No No No
## 6 Current K-12 Democrat No
## Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1 Yes Public No Yes No No No Yes
## 2 Yes Public No Yes No Yes No No Yes
## 3 Yes Private No No No Yes No No Yes
## 4 No Public No Yes No Yes No No Yes
## 5 Yes Public No Yes No Yes Yes No Yes
## 6 Yes Public No No No Yes No Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1 Try first No No Yes Yes
## 2 Science Study first Yes Yes No No Receiving No
## 3 Science Study first Yes No Yes Receiving No
## 4 Science Try first No Yes Yes No Giving Yes
## 5 Art Try first Yes No No No Giving No
## 6 Science Try first Yes Yes No Yes Receiving No
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 1 Yes Idealist No No Yes
## 2 No Pragmatist No No Cool headed Standard hours No
## 3 Yes Pragmatist No Yes Cool headed Odd hours No
## 4 No Idealist No No Cool headed Standard hours No
## 5 No Idealist Yes Yes Hot headed Standard hours No
## 6 No Pragmatist No No Standard hours
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1 Happy Yes Yes No No P.M. Yes Start Yes
## 2 Happy Yes Yes Yes No A.M. No End Yes
## 3 Right Yes No No Yes A.M. Yes Start Yes
## 4 Happy Yes Yes No No A.M. Yes Start Yes
## 5 Happy Yes Yes No Yes P.M. No End No
## 6
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1 No Circumstances Yes Yes Yes Yes No
## 2 No Me Yes Yes No Yes No Mysterious
## 3 Yes Circumstances No Yes No Yes Yes Mysterious
## 4 No Circumstances Yes No No Yes No TMI
## 5 No Me No Yes Yes Yes Yes TMI
## 6
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1 Yes Yes Talk Technology No No Yes
## 2 No No
## 3 No No Tunes Technology Yes Yes Yes Yes
## 4 No No Talk People No Yes Yes Yes
## 5 Yes No Tunes People No No Yes No
## 6
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1 No Demanding No No Cautious No Yes!
## 2 Mac Yes Cautious No Umm...
## 3 No Supportive No PC No Cautious No Umm...
## 4 Yes Supportive No Mac Yes Risk-friendly No Umm...
## 5 No Demanding Yes PC Yes Cautious No Yes!
## 6 Yes Supportive No PC
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1 No Space No In-person Yes No Yes
## 2 No Space Yes In-person No Yes Yes No
## 3 No Space No In-person No No Yes Yes
## 4 No Socialize Yes Online No Yes No Yes
## 5 No Socialize No Online No No Yes Yes
## 6 In-person No No Yes Yes
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1 Yay people! Yes No Yes Yes No Yes
## 2 Yay people! Yes Yes Yes Yes Yes No Yes
## 3 Grrr people Yes No No No No No No
## 4 Grrr people No No Yes Yes No Yes Yes
## 5 Yay people! Yes No Yes Yes Yes Yes No
## 6 Grrr people Yes No Yes Yes No No Yes
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1 No No No Yes No Own Optimist
## 2
## 3 Yes No No Yes No Own Pessimist Mom
## 4 No No No Yes Yes Own Optimist Mom
## 5 No No Yes No No Own Optimist Mom
## 6 Yes Yes No Yes
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1 Yes Yes No No Nope Yes No No
## 2 No
## 3 No No No No Nope Yes No No No
## 4 No No No Yes Check! No No No Yes
## 5 No Yes Yes Yes Nope Yes No No Yes
## 6
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1 No Only-child No No Yes
## 2 No No Only-child Yes No No
## 3 Yes No Yes No Yes No
## 4 Yes No Yes No No Yes
## 5 No No Yes No No Yes
## 6
## USER_ID YOB Gender Income HouseholdStatus
## 193 245 1964 Male over $150,000 Married (w/kids)
## 848 1046 1953 Male $100,001 - $150,000 Domestic Partners (no kids)
## 2836 3530 1995 Male Single (no kids)
## 4052 5050 1945 Female $75,000 - $100,000 Married (w/kids)
## 4093 5107 1980 Female $100,001 - $150,000 Married (w/kids)
## 5509 6888 1998 Female under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 193 Bachelor's Degree Republican Yes Yes No Yes
## 848 Democrat
## 2836 Current Undergraduate Democrat Yes Yes Yes No
## 4052 Bachelor's Degree Republican
## 4093 Bachelor's Degree Democrat No No
## 5509 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 193 No Yes Public No Yes No Yes No
## 848
## 2836 Yes Public Yes No No Yes Yes
## 4052 No Public
## 4093 No No Private No
## 5509 Yes Yes
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 193 No Yes Science Try first Yes Yes Yes No
## 848
## 2836 Yes Yes Art Study first No Yes Yes
## 4052
## 4093 Yes
## 5509 Yes No Art Study first Yes No Yes No
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186
## 193 Giving Yes No Idealist Yes Yes Hot headed
## 848
## 2836 Yes Yes Idealist Yes No Cool headed
## 4052 No No No
## 4093 No No Pragmatist No Yes
## 5509 Giving No
## Q117193 Q116797 Q116881 Q116953 Q116601 Q116441 Q116448
## 193 Standard hours No Happy Yes Yes No No
## 848
## 2836 Odd hours No Happy Yes Yes No
## 4052
## 4093
## 5509
## Q116197 Q115602 Q115777 Q115610 Q115611 Q115899 Q115390 Q114961
## 193 A.M. Yes End Yes Yes Me No No
## 848
## 2836 Yes End Yes No Circumstances Yes No
## 4052 P.M. Yes Start Yes No No
## 4093 P.M. Yes Start Yes No Circumstances
## 5509
## Q114748 Q115195 Q114517 Q114386 Q113992 Q114152 Q113583 Q113584
## 193 Yes No Yes TMI No Yes Tunes Technology
## 848
## 2836 Yes No No Mysterious No Yes Tunes People
## 4052 No Yes
## 4093 Tunes People
## 5509
## Q113181 Q112478 Q112512 Q112270 Q111848 Q111580 Q111220 Q110740
## 193 No Yes Yes Yes Supportive No Mac
## 848
## 2836 Yes Yes Yes No Yes Demanding Yes PC
## 4052
## 4093 Yes Supportive
## 5509
## Q109367 Q108950 Q109244 Q108855 Q108617 Q108856 Q108754
## 193 No Cautious No Yes! No Socialize No
## 848 Yes Risk-friendly Yes Yes! No Space No
## 2836 Yes Cautious Yes Yes
## 4052
## 4093 No Risk-friendly No Yes! No Space No
## 5509
## Q108342 Q108343 Q107869 Q107491 Q106993 Q106997 Q106272 Q106388
## 193 In-person No Yes Yes No Yay people! Yes Yes
## 848 In-person Yes
## 2836 In-person Yes Yes Yes No
## 4052 No Grrr people
## 4093 In-person Yes Yes Yes Yes Yay people! Yes Yes
## 5509
## Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906 Q102674
## 193 No Yes No No Yes No No No
## 848
## 2836 Yes No No No Yes Yes No No
## 4052 No No No No
## 4093 No No No No Yes No No Yes
## 5509
## Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689 Q100680
## 193 No No Own Optimist Dad Yes Yes No
## 848
## 2836 Yes Yes Rent Optimist Dad No Yes Yes
## 4052 Yes Own No
## 4093 Yes Yes Rent No Yes
## 5509
## Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 193 Yes Check! No No No Yes Yes No Yes
## 848
## 2836 Yes Check! No No No Yes Yes Yes
## 4052
## 4093 No Nope Yes No Yes Yes Yes No Yes
## 5509
## Q98078 Q98197 Q96024
## 193 No Yes Yes
## 848 No
## 2836 Yes Yes No
## 4052
## 4093 Yes Yes No
## 5509
## USER_ID YOB Gender Income HouseholdStatus
## 5563 6955 1966 Male over $150,000 Married (w/kids)
## 5564 6956 NA Male
## 5565 6957 2000 Female
## 5566 6958 1969 Male over $150,000
## 5567 6959 1986 Male $25,001 - $50,000 Married (w/kids)
## 5568 6960 1999 Male under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 5563 Bachelor's Degree Democrat
## 5564 Master's Degree Democrat No No
## 5565 Current K-12 Republican
## 5566 Bachelor's Degree Democrat Yes
## 5567 High School Diploma Republican
## 5568 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 5563 No Yes No Yes Yes
## 5564 No Yes Public Yes
## 5565 Public Yes
## 5566 No No No Yes Yes
## 5567 Yes Yes No
## 5568 Yes No No
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 5563
## 5564
## 5565 Yes Yes Art Try first No Yes Yes Yes
## 5566 Yes Yes Science
## 5567 No No Science No Yes
## 5568
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186 Q117193
## 5563
## 5564
## 5565 Receiving
## 5566
## 5567
## 5568
## Q116797 Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q115777 Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q114517 Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q112512 Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## 'data.frame': 5568 obs. of 20 variables:
## $ USER_ID : int 1 4 5 8 9 10 11 12 13 15 ...
## $ YOB : int 1938 1970 1997 1983 1984 1997 1983 1996 NA 1981 ...
## $ Gender : chr "Male" "Female" "Male" "Male" ...
## $ Income : chr "" "over $150,000" "$75,000 - $100,000" "$100,001 - $150,000" ...
## $ HouseholdStatus: chr "Married (w/kids)" "Domestic Partners (w/kids)" "Single (no kids)" "Married (w/kids)" ...
## $ EducationLevel : chr "" "Bachelor's Degree" "High School Diploma" "Bachelor's Degree" ...
## $ Party : chr "Democrat" "Democrat" "Republican" "Democrat" ...
## $ Q124742 : chr "No" "" "" "No" ...
## $ Q124122 : chr "" "Yes" "Yes" "Yes" ...
## $ Q123464 : chr "No" "No" "Yes" "No" ...
## $ Q123621 : chr "No" "No" "No" "Yes" ...
## $ Q122769 : chr "No" "No" "" "No" ...
## $ Q122770 : chr "Yes" "Yes" "Yes" "No" ...
## $ Q122771 : chr "Public" "Public" "Private" "Public" ...
## $ Q122120 : chr "No" "No" "No" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "No" "No" "No" "No" ...
## $ Q120978 : chr "" "Yes" "Yes" "Yes" ...
## $ Q121011 : chr "No" "No" "No" "No" ...
## $ Q120379 : chr "No" "No" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 20 variables:
## $ Q120650: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q118117: chr "Yes" "No" "Yes" "No" ...
## $ Q118233: chr "No" "No" "No" "No" ...
## $ Q118237: chr "No" "No" "Yes" "No" ...
## $ Q116441: chr "No" "Yes" "No" "No" ...
## $ Q116197: chr "P.M." "A.M." "A.M." "A.M." ...
## $ Q115611: chr "No" "No" "Yes" "No" ...
## $ Q115899: chr "Circumstances" "Me" "Circumstances" "Circumstances" ...
## $ Q115390: chr "Yes" "Yes" "No" "Yes" ...
## $ Q114748: chr "Yes" "No" "No" "No" ...
## $ Q115195: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q113584: chr "Technology" "" "Technology" "People" ...
## $ Q112478: chr "No" "" "Yes" "Yes" ...
## $ Q112270: chr "" "" "Yes" "Yes" ...
## $ Q111848: chr "No" "" "No" "Yes" ...
## $ Q106993: chr "Yes" "No" "Yes" "Yes" ...
## $ Q106388: chr "No" "Yes" "No" "No" ...
## $ Q105655: chr "No" "No" "No" "Yes" ...
## $ Q104996: chr "Yes" "Yes" "No" "Yes" ...
## $ Q102674: chr "No" "" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 21 variables:
## $ Q102674: chr "No" "" "No" "No" ...
## $ Q102687: chr "Yes" "" "Yes" "Yes" ...
## $ Q102289: chr "No" "" "No" "Yes" ...
## $ Q102089: chr "Own" "" "Own" "Own" ...
## $ Q101162: chr "Optimist" "" "Pessimist" "Optimist" ...
## $ Q101163: chr "" "" "Mom" "Mom" ...
## $ Q101596: chr "Yes" "" "No" "No" ...
## $ Q100689: chr "Yes" "" "No" "No" ...
## $ Q100680: chr "No" "" "No" "No" ...
## $ Q100562: chr "No" "" "No" "Yes" ...
## $ Q99982 : chr "Nope" "" "Nope" "Check!" ...
## $ Q100010: chr "Yes" "" "Yes" "No" ...
## $ Q99716 : chr "No" "" "No" "No" ...
## $ Q99581 : chr "No" "" "No" "No" ...
## $ Q99480 : chr "" "No" "No" "Yes" ...
## $ Q98869 : chr "No" "No" "Yes" "Yes" ...
## $ Q98578 : chr "" "No" "No" "No" ...
## $ Q98059 : chr "Only-child" "Only-child" "Yes" "Yes" ...
## $ Q98078 : chr "No" "Yes" "No" "No" ...
## $ Q98197 : chr "No" "No" "Yes" "No" ...
## $ Q96024 : chr "Yes" "No" "No" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Reading file ./data/test2016.csv..."
## [1] "dimensions of data in ./data/test2016.csv: 1,392 rows x 107 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 2 1985 Female $25,001 - $50,000 Single (no kids)
## 2 3 1983 Male $50,000 - $74,999 Married (w/kids)
## 3 6 1995 Male $75,000 - $100,000 Single (no kids)
## 4 7 1980 Female $50,000 - $74,999 Single (no kids)
## 5 14 1980 Female Married (no kids)
## 6 28 1973 Male over $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1 Master's Degree Yes No Yes No No
## 2 Current Undergraduate No Yes Yes
## 3 Current K-12
## 4 Master's Degree Yes Yes No Yes Yes Yes
## 5 Current Undergraduate Yes No Yes No No
## 6 Master's Degree No Yes No Yes No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650 Q120472
## 1 Public No Yes Yes Yes No Yes Yes Science
## 2 Public No Yes No
## 3 No No No Yes No Yes Science
## 4 Public No Yes No Yes No Yes Yes Science
## 5 Public Yes Yes No Yes Yes No Yes Art
## 6 Public No Yes No Yes Yes Yes Yes Science
## Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892 Q118117
## 1 Study first Yes Yes Yes No Giving Yes No
## 2 Study first No Yes No
## 3 Try first No Yes No Yes Giving
## 4 Try first Yes No No Yes Giving Yes Yes
## 5 Try first Yes Yes Yes Yes Giving No No
## 6 Try first Yes Yes No No Giving No Yes
## Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1 Idealist No Yes Cool headed Odd hours Yes Happy
## 2
## 3
## 4 Idealist No No Cool headed Standard hours No Happy
## 5 Idealist No Yes Hot headed Standard hours Yes Happy
## 6 Pragmatist Yes No Hot headed Odd hours Yes Right
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610 Q115611
## 1 Yes Yes No Yes A.M. Yes End Yes No
## 2 Yes Yes P.M.
## 3 Yes
## 4 Yes No No Yes A.M. Yes Start Yes No
## 5 Yes Yes Yes No P.M. Yes End No No
## 6 Yes Yes Yes Yes P.M. End Yes Yes
## Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386 Q113992
## 1 Me No Yes No Yes Yes TMI
## 2 No Yes
## 3 Yes No Yes Yes No TMI No
## 4 Me Yes No Yes Yes Yes TMI No
## 5 Me No No No Yes No TMI No
## 6 Circumstances No Yes No Yes No TMI Yes
## Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270 Q111848
## 1 No Tunes People Yes Yes No Yes Yes
## 2 No No No Yes
## 3 No Tunes Technology Yes No Yes No
## 4 Yes Talk People No No Yes No Yes
## 5 Tunes Technology No Yes Yes Yes
## 6 No Talk Technology No Yes Yes No Yes
## Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855 Q108617
## 1 Supportive No Yes Cautious Yes Yes!
## 2 No Yes Cautious No Yes! No
## 3 No No No
## 4 Supportive No PC No Cautious Yes Yes! No
## 5 Supportive Yes Mac Yes Cautious No Yes! No
## 6 Demanding No PC Yes Cautious No Umm... No
## Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993 Q106997
## 1 Yes In-person Yes
## 2 Space No Yes Yes Yes Grrr people
## 3 Yes In-person No No Yes Yes Yay people!
## 4 Space No Online No No Yes Yes Yay people!
## 5 Space No In-person No No Yes No Grrr people
## 6 Space No In-person Yes Yes Yes Grrr people
## Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906
## 1
## 2 Yes No No Yes No Yes No No
## 3 Yes No Yes No No Yes Yes No No
## 4 No No No No No Yes Yes No No
## 5 No No No Yes Yes Yes Yes Yes No
## 6 Yes No Yes Yes No No No Yes Yes
## Q102674 Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689
## 1 No
## 2 Rent Pessimist Dad
## 3 No No Yes Own Optimist Mom No No
## 4 No No No Own Optimist Dad No No
## 5 Yes No No Own Pessimist Mom No Yes
## 6 Yes Yes No Own Pessimist Mom No Yes
## Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 1 Yes Yes Yes
## 2 Yes Yes Yes
## 3 Yes Yes Nope No No No Yes Yes No Yes
## 4 Yes Yes Nope Yes No No No Yes No Yes
## 5 Yes Yes Nope Yes No No Yes No No Yes
## 6 Yes Yes Nope Yes No No Yes No No Yes
## Q98078 Q98197 Q96024
## 1
## 2 Yes No Yes
## 3 No Yes Yes
## 4 No No Yes
## 5 No No No
## 6 No No Yes
## USER_ID YOB Gender Income HouseholdStatus
## 503 2555 1956 Male over $150,000 Married (w/kids)
## 515 2616 1959 Male over $150,000 Married (w/kids)
## 857 4346 1990 Female $50,000 - $74,999
## 950 4814 1969 Male $75,000 - $100,000 Married (w/kids)
## 1207 6057 1937 Female $25,001 - $50,000 Married (no kids)
## 1255 6285 1976 Female $100,001 - $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 503 Bachelor's Degree No No No Yes No Yes
## 515 Bachelor's Degree
## 857 Bachelor's Degree
## 950 Bachelor's Degree Yes No Yes No No
## 1207 Bachelor's Degree No Yes
## 1255 Bachelor's Degree
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 503 Private No Yes No No Yes No Yes
## 515 No No
## 857 No Yes No No No No Yes
## 950 Public Yes Yes No Yes Yes No Yes
## 1207 Public No Yes No No No No
## 1255
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 503 Science Study first No Yes No Yes Giving Yes
## 515 Yes
## 857 Science Study first No No Yes No Receiving Yes
## 950 Science Study first No No No No Giving No
## 1207 Study first No No Yes Receiving Yes
## 1255
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 503 No Pragmatist No No Cool headed Standard hours No
## 515 No Pragmatist No Yes Cool headed Standard hours No
## 857 Yes Pragmatist No No Cool headed Odd hours No
## 950 No Pragmatist No Yes Hot headed Odd hours Yes
## 1207 No Pragmatist No No Hot headed No
## 1255
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777
## 503 Happy Yes Yes No No A.M. Yes End
## 515 Right Yes Yes No Yes Yes
## 857 Right Yes Yes No No A.M. Yes Start
## 950 Happy Yes Yes Yes No P.M. Yes Start
## 1207 Happy Yes Yes No No A.M. Yes Start
## 1255 Yes No Yes A.M. Yes Start
## Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517
## 503 Yes Yes Me No No No Yes Yes
## 515 Yes No Me Yes No Yes Yes No
## 857 Yes No Me No No No Yes
## 950 Yes No Me Yes No Yes No No
## 1207 No No Circumstances Yes No Yes No Yes
## 1255 Yes No Circumstances No Yes No Yes Yes
## Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512
## 503 TMI Yes Yes Tunes People Yes No Yes
## 515 No Yes Talk Technology
## 857 Mysterious No No Tunes People No No No
## 950 Mysterious No No Tunes People Yes Yes Yes
## 1207 Yes No Talk Yes
## 1255 TMI Yes Yes Yes
## Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 503 No Yes Demanding No PC No Cautious
## 515 No Yes No Mac Yes
## 857 Yes Yes Supportive No Mac No Risk-friendly
## 950 No Yes Supportive Yes PC No Cautious
## 1207 Supportive No PC Cautious
## 1255 Yes Yes Demanding No Mac
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 503 No Umm... No Space No In-person No Yes
## 515
## 857 Yes Umm... No Space No In-person No Yes
## 950 No Yes! No Space No In-person No No
## 1207 Yes! No Space No In-person No Yes
## 1255
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 503 Yes Yes Yay people! Yes No No Yes No
## 515 No
## 857 No Yes Grrr people Yes No Yes No No
## 950 Yes No Grrr people Yes Yes No No No
## 1207 Yes Yes Yes
## 1255
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 503 No Yes No No No Yes No Own
## 515 Yes Yes
## 857 No Yes Yes No No Yes Yes Own
## 950 Yes Yes Yes No No Yes No Own
## 1207 Yes
## 1255
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010
## 503 Pessimist Mom Yes Yes No Yes Check! Yes
## 515 Check! Yes
## 857 Optimist Mom No Yes Yes No Nope Yes
## 950 Pessimist Mom Yes No No No Check! Yes
## 1207
## 1255
## Q99716 Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 503 No No Yes Yes No Yes Yes Yes Yes
## 515 No Yes Yes Yes No Yes Yes
## 857 No Yes Yes Yes No Yes No No No
## 950 No No Yes Yes No Yes No Yes Yes
## 1207
## 1255
## USER_ID YOB Gender Income HouseholdStatus
## 1387 6922 1988 Male $50,000 - $74,999 Single (no kids)
## 1388 6928 1977 Female $50,000 - $74,999 Domestic Partners (no kids)
## 1389 6930 1998 Female $100,001 - $150,000 Single (no kids)
## 1390 6941 1989 Male $25,001 - $50,000 Married (no kids)
## 1391 6946 1996 Male
## 1392 6947 NA Female
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1387 Master's Degree
## 1388 Master's Degree
## 1389 Current K-12 No No
## 1390 Bachelor's Degree
## 1391 Current K-12
## 1392 Yes Yes No No No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1387 Yes Yes Yes Yes Yes Yes
## 1388 Yes No Yes
## 1389 Public Yes Yes Yes Yes Yes Yes Yes
## 1390 Yes Yes No No No
## 1391 Yes No No Yes No Yes Yes
## 1392 Public Yes Yes No Yes Yes Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1387 Science Try first No Yes Yes No Giving
## 1388 Art
## 1389 Art Study first Yes No Yes No Giving
## 1390
## 1391 Art Study first Yes Yes Yes No Giving
## 1392 Art No No No Yes Giving
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## 'data.frame': 1392 obs. of 20 variables:
## $ USER_ID : int 2 3 6 7 14 28 29 37 44 56 ...
## $ YOB : int 1985 1983 1995 1980 1980 1973 1968 1961 1989 1975 ...
## $ Gender : chr "Female" "Male" "Male" "Female" ...
## $ Income : chr "$25,001 - $50,000" "$50,000 - $74,999" "$75,000 - $100,000" "$50,000 - $74,999" ...
## $ HouseholdStatus: chr "Single (no kids)" "Married (w/kids)" "Single (no kids)" "Single (no kids)" ...
## $ EducationLevel : chr "Master's Degree" "Current Undergraduate" "Current K-12" "Master's Degree" ...
## $ Q124742 : chr "" "" "" "Yes" ...
## $ Q124122 : chr "Yes" "" "" "Yes" ...
## $ Q123464 : chr "No" "No" "" "No" ...
## $ Q123621 : chr "Yes" "" "" "Yes" ...
## $ Q122769 : chr "No" "Yes" "" "Yes" ...
## $ Q122770 : chr "No" "Yes" "" "Yes" ...
## $ Q122771 : chr "Public" "Public" "" "Public" ...
## $ Q122120 : chr "No" "No" "" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "Yes" "No" "No" "No" ...
## $ Q120978 : chr "Yes" "" "No" "Yes" ...
## $ Q121011 : chr "No" "" "Yes" "No" ...
## $ Q120379 : chr "Yes" "" "No" "Yes" ...
## $ Q120650 : chr "Yes" "" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 20 variables:
## $ Q120012: chr "Yes" "No" "No" "Yes" ...
## $ Q120014: chr "Yes" "Yes" "Yes" "No" ...
## $ Q118117: chr "No" "" "" "Yes" ...
## $ Q118237: chr "Yes" "" "" "No" ...
## $ Q116953: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q116601: chr "Yes" "Yes" "" "No" ...
## $ Q116448: chr "Yes" "" "" "Yes" ...
## $ Q116197: chr "A.M." "P.M." "" "A.M." ...
## $ Q115899: chr "Me" "" "" "Me" ...
## $ Q114961: chr "Yes" "" "No" "No" ...
## $ Q113584: chr "People" "" "Technology" "People" ...
## $ Q113181: chr "Yes" "No" "Yes" "No" ...
## $ Q112512: chr "No" "" "Yes" "Yes" ...
## $ Q108950: chr "Cautious" "Cautious" "" "Cautious" ...
## $ Q108617: chr "" "No" "No" "No" ...
## $ Q108342: chr "In-person" "" "In-person" "Online" ...
## $ Q107491: chr "" "Yes" "Yes" "Yes" ...
## $ Q106272: chr "" "Yes" "Yes" "No" ...
## $ Q106389: chr "" "No" "Yes" "No" ...
## $ Q104996: chr "" "No" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 21 variables:
## $ Q102674: chr "" "" "No" "No" ...
## $ Q102687: chr "" "" "No" "No" ...
## $ Q102289: chr "" "" "Yes" "No" ...
## $ Q102089: chr "" "Rent" "Own" "Own" ...
## $ Q101162: chr "" "Pessimist" "Optimist" "Optimist" ...
## $ Q101163: chr "" "Dad" "Mom" "Dad" ...
## $ Q101596: chr "" "" "No" "No" ...
## $ Q100689: chr "No" "" "No" "No" ...
## $ Q100680: chr "Yes" "" "Yes" "Yes" ...
## $ Q100562: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q99982 : chr "" "" "Nope" "Nope" ...
## $ Q100010: chr "" "" "No" "Yes" ...
## $ Q99716 : chr "" "" "No" "No" ...
## $ Q99581 : chr "" "" "No" "No" ...
## $ Q99480 : chr "" "" "Yes" "No" ...
## $ Q98869 : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q98578 : chr "" "" "No" "No" ...
## $ Q98059 : chr "" "Yes" "Yes" "Yes" ...
## $ Q98078 : chr "" "Yes" "No" "No" ...
## $ Q98197 : chr "" "No" "Yes" "No" ...
## $ Q96024 : chr "" "Yes" "Yes" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: YOB.Age.fctr..."
## [1] "Creating new feature: YOB.Age.dff..."
## [1] "Creating new feature: Gender.fctr..."
## [1] "Creating new feature: Income.fctr..."
## [1] "Creating new feature: Hhold.fctr..."
## [1] "Creating new feature: Edn.fctr..."
## [1] "Creating new feature: Q124742.fctr..."
## [1] "Creating new feature: Q124122.fctr..."
## [1] "Creating new feature: Q123621.fctr..."
## [1] "Creating new feature: Q123464.fctr..."
## [1] "Creating new feature: Q122771.fctr..."
## [1] "Creating new feature: Q122770.fctr..."
## [1] "Creating new feature: Q122769.fctr..."
## [1] "Creating new feature: Q122120.fctr..."
## [1] "Creating new feature: Q121700.fctr..."
## [1] "Creating new feature: Q121699.fctr..."
## [1] "Creating new feature: Q121011.fctr..."
## [1] "Creating new feature: Q120978.fctr..."
## [1] "Creating new feature: Q120650.fctr..."
## [1] "Creating new feature: Q120472.fctr..."
## [1] "Creating new feature: Q120379.fctr..."
## [1] "Creating new feature: Q120194.fctr..."
## [1] "Creating new feature: Q120014.fctr..."
## [1] "Creating new feature: Q120012.fctr..."
## [1] "Creating new feature: Q119851.fctr..."
## [1] "Creating new feature: Q119650.fctr..."
## [1] "Creating new feature: Q119334.fctr..."
## [1] "Creating new feature: Q118892.fctr..."
## [1] "Creating new feature: Q118237.fctr..."
## [1] "Creating new feature: Q118233.fctr..."
## [1] "Creating new feature: Q118232.fctr..."
## [1] "Creating new feature: Q118117.fctr..."
## [1] "Creating new feature: Q117193.fctr..."
## [1] "Creating new feature: Q117186.fctr..."
## [1] "Creating new feature: Q116797.fctr..."
## [1] "Creating new feature: Q116881.fctr..."
## [1] "Creating new feature: Q116953.fctr..."
## [1] "Creating new feature: Q116601.fctr..."
## [1] "Creating new feature: Q116441.fctr..."
## [1] "Creating new feature: Q116448.fctr..."
## [1] "Creating new feature: Q116197.fctr..."
## [1] "Creating new feature: Q115602.fctr..."
## [1] "Creating new feature: Q115777.fctr..."
## [1] "Creating new feature: Q115610.fctr..."
## [1] "Creating new feature: Q115611.fctr..."
## [1] "Creating new feature: Q115899.fctr..."
## [1] "Creating new feature: Q115390.fctr..."
## [1] "Creating new feature: Q115195.fctr..."
## [1] "Creating new feature: Q114961.fctr..."
## [1] "Creating new feature: Q114748.fctr..."
## [1] "Creating new feature: Q114517.fctr..."
## [1] "Creating new feature: Q114386.fctr..."
## [1] "Creating new feature: Q114152.fctr..."
## [1] "Creating new feature: Q113992.fctr..."
## [1] "Creating new feature: Q113583.fctr..."
## [1] "Creating new feature: Q113584.fctr..."
## [1] "Creating new feature: Q113181.fctr..."
## [1] "Creating new feature: Q112478.fctr..."
## [1] "Creating new feature: Q112512.fctr..."
## [1] "Creating new feature: Q112270.fctr..."
## [1] "Creating new feature: Q111848.fctr..."
## [1] "Creating new feature: Q111580.fctr..."
## [1] "Creating new feature: Q111220.fctr..."
## [1] "Creating new feature: Q110740.fctr..."
## [1] "Creating new feature: Q109367.fctr..."
## [1] "Creating new feature: Q109244.fctr..."
## [1] "Creating new feature: Q108950.fctr..."
## [1] "Creating new feature: Q108855.fctr..."
## [1] "Creating new feature: Q108617.fctr..."
## [1] "Creating new feature: Q108856.fctr..."
## [1] "Creating new feature: Q108754.fctr..."
## [1] "Creating new feature: Q108342.fctr..."
## [1] "Creating new feature: Q108343.fctr..."
## [1] "Creating new feature: Q107869.fctr..."
## [1] "Creating new feature: Q107491.fctr..."
## [1] "Creating new feature: Q106993.fctr..."
## [1] "Creating new feature: Q106997.fctr..."
## [1] "Creating new feature: Q106272.fctr..."
## [1] "Creating new feature: Q106388.fctr..."
## [1] "Creating new feature: Q106389.fctr..."
## [1] "Creating new feature: Q106042.fctr..."
## [1] "Creating new feature: Q105840.fctr..."
## [1] "Creating new feature: Q105655.fctr..."
## [1] "Creating new feature: Q104996.fctr..."
## [1] "Creating new feature: Q103293.fctr..."
## [1] "Creating new feature: Q102906.fctr..."
## [1] "Creating new feature: Q102674.fctr..."
## [1] "Creating new feature: Q102687.fctr..."
## [1] "Creating new feature: Q102289.fctr..."
## [1] "Creating new feature: Q102089.fctr..."
## [1] "Creating new feature: Q101162.fctr..."
## [1] "Creating new feature: Q101163.fctr..."
## [1] "Creating new feature: Q101596.fctr..."
## [1] "Creating new feature: Q100689.fctr..."
## [1] "Creating new feature: Q100680.fctr..."
## [1] "Creating new feature: Q100562.fctr..."
## [1] "Creating new feature: Q100010.fctr..."
## [1] "Creating new feature: Q99982.fctr..."
## [1] "Creating new feature: Q99716.fctr..."
## [1] "Creating new feature: Q99581.fctr..."
## [1] "Creating new feature: Q99480.fctr..."
## [1] "Creating new feature: Q98869.fctr..."
## [1] "Creating new feature: Q98578.fctr..."
## [1] "Creating new feature: Q98197.fctr..."
## [1] "Creating new feature: Q98059.fctr..."
## [1] "Creating new feature: Q98078.fctr..."
## [1] "Creating new feature: Q96024.fctr..."## [1] "Partition stats:"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392
## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392## Loading required package: RColorBrewer
## .src .n
## 1 Train 5568
## 2 Test 1392## [1] "Running glbObsDropCondition filter: (glbObsAll[, \"Q109244\"] != \"\")"
## [1] "Partition stats:"
## Party .src .n
## 1 Democrat Train 1171
## 2 Republican Train 1013
## 3 <NA> Test 547
## Party .src .n
## 1 Democrat Train 1171
## 2 Republican Train 1013
## 3 <NA> Test 547
## .src .n
## 1 Train 2184
## 2 Test 547## Loading required package: lazyeval## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## [1] "Found 0 duplicates by all features:"## NULL## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 7.843 19.975 12.132
## 2 inspect.data 2 0 0 19.975 NA NAStep 2.0: inspect data
## Warning: Removed 547 rows containing non-finite values (stat_count).## Loading required package: reshape2
## Party.Democrat Party.Republican Party.NA
## Test NA NA 547
## Train 1171 1013 NA
## Party.Democrat Party.Republican Party.NA
## Test NA NA 1
## Train 0.5361722 0.4638278 NA
## [1] "numeric data missing in : "
## YOB
## 239
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024
## 2084 2065## Party Party.fctr .n
## 1 Democrat D 1171
## 2 Republican R 1013
## 3 <NA> <NA> 547## Warning: Removed 1 rows containing missing values (position_stack).
## Party.fctr.D Party.fctr.R Party.fctr.NA
## Test NA NA 547
## Train 1171 1013 NA
## Party.fctr.D Party.fctr.R Party.fctr.NA
## Test NA NA 1
## Train 0.5361722 0.4638278 NA
## [1] "elapsed Time (secs): 9.169000"## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "elapsed Time (secs): 139.788000"
## [1] "elapsed Time (secs): 139.788000"## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 19.975 176.308 156.333
## 3 scrub.data 2 1 1 176.309 NA NAStep 2.1: scrub data
```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## [1] "numeric data missing in : "
## YOB Party.fctr
## 239 547
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024
## 2084 2065## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 176.309 222.429 46.121
## 4 transform.data 2 2 2 222.430 NA NAStep 2.2: transform data
## label step_major step_minor label_minor bgn end
## 4 transform.data 2 2 2 222.430 222.473
## 5 extract.features 3 0 0 222.474 NA
## elapsed
## 4 0.044
## 5 NAStep 3.0: extract features
## label step_major step_minor label_minor bgn
## 5 extract.features 3 0 0 222.474
## 6 extract.features.datetime 3 1 1 222.496
## end elapsed
## 5 222.495 0.021
## 6 NA NAStep 3.1: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 222.525
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 6 extract.features.datetime 3 1 1 222.496
## 7 extract.features.image 3 2 2 222.539
## end elapsed
## 6 222.538 0.042
## 7 NA NAStep 3.2: extract features image
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end
## 1 extract.features.image.bgn 1 0 0 222.574 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 222.574
## 2 extract.features.image.end 2 0 0 222.584
## end elapsed
## 1 222.583 0.01
## 2 NA NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 222.574
## 2 extract.features.image.end 2 0 0 222.584
## end elapsed
## 1 222.583 0.01
## 2 NA NA## label step_major step_minor label_minor bgn end
## 7 extract.features.image 3 2 2 222.539 222.595
## 8 extract.features.price 3 3 3 222.595 NA
## elapsed
## 7 0.056
## 8 NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 222.623 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 222.595 222.632
## 9 extract.features.text 3 4 4 222.633 NA
## elapsed
## 8 0.037
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 222.68 NA
## elapsed
## 1 NA## Warning in rm(tmp_allobs_df): object 'tmp_allobs_df' not found## Warning in rm(tmp_trnobs_df): object 'tmp_trnobs_df' not found## label step_major step_minor label_minor bgn
## 9 extract.features.text 3 4 4 222.633
## 10 extract.features.string 3 5 5 222.695
## end elapsed
## 9 222.695 0.062
## 10 NA NAStep 3.5: extract features string
## label step_major step_minor label_minor bgn
## 1 extract.features.string.bgn 1 0 0 222.733
## end elapsed
## 1 NA NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 222.733 222.742 0.009
## 2 0 222.742 NA NA## Gender Income HouseholdStatus EducationLevel
## "Gender" "Income" "HouseholdStatus" "EducationLevel"
## Party Q124742 Q124122 Q123464
## "Party" "Q124742" "Q124122" "Q123464"
## Q123621 Q122769 Q122770 Q122771
## "Q123621" "Q122769" "Q122770" "Q122771"
## Q122120 Q121699 Q121700 Q120978
## "Q122120" "Q121699" "Q121700" "Q120978"
## Q121011 Q120379 Q120650 Q120472
## "Q121011" "Q120379" "Q120650" "Q120472"
## Q120194 Q120012 Q120014 Q119334
## "Q120194" "Q120012" "Q120014" "Q119334"
## Q119851 Q119650 Q118892 Q118117
## "Q119851" "Q119650" "Q118892" "Q118117"
## Q118232 Q118233 Q118237 Q117186
## "Q118232" "Q118233" "Q118237" "Q117186"
## Q117193 Q116797 Q116881 Q116953
## "Q117193" "Q116797" "Q116881" "Q116953"
## Q116601 Q116441 Q116448 Q116197
## "Q116601" "Q116441" "Q116448" "Q116197"
## Q115602 Q115777 Q115610 Q115611
## "Q115602" "Q115777" "Q115610" "Q115611"
## Q115899 Q115390 Q114961 Q114748
## "Q115899" "Q115390" "Q114961" "Q114748"
## Q115195 Q114517 Q114386 Q113992
## "Q115195" "Q114517" "Q114386" "Q113992"
## Q114152 Q113583 Q113584 Q113181
## "Q114152" "Q113583" "Q113584" "Q113181"
## Q112478 Q112512 Q112270 Q111848
## "Q112478" "Q112512" "Q112270" "Q111848"
## Q111580 Q111220 Q110740 Q109367
## "Q111580" "Q111220" "Q110740" "Q109367"
## Q108950 Q109244 Q108855 Q108617
## "Q108950" "Q109244" "Q108855" "Q108617"
## Q108856 Q108754 Q108342 Q108343
## "Q108856" "Q108754" "Q108342" "Q108343"
## Q107869 Q107491 Q106993 Q106997
## "Q107869" "Q107491" "Q106993" "Q106997"
## Q106272 Q106388 Q106389 Q106042
## "Q106272" "Q106388" "Q106389" "Q106042"
## Q105840 Q105655 Q104996 Q103293
## "Q105840" "Q105655" "Q104996" "Q103293"
## Q102906 Q102674 Q102687 Q102289
## "Q102906" "Q102674" "Q102687" "Q102289"
## Q102089 Q101162 Q101163 Q101596
## "Q102089" "Q101162" "Q101163" "Q101596"
## Q100689 Q100680 Q100562 Q99982
## "Q100689" "Q100680" "Q100562" "Q99982"
## Q100010 Q99716 Q99581 Q99480
## "Q100010" "Q99716" "Q99581" "Q99480"
## Q98869 Q98578 Q98059 Q98078
## "Q98869" "Q98578" "Q98059" "Q98078"
## Q98197 Q96024 .src
## "Q98197" "Q96024" ".src"## label step_major step_minor label_minor bgn
## 10 extract.features.string 3 5 5 222.695
## 11 extract.features.end 3 6 6 222.765
## end elapsed
## 10 222.765 0.07
## 11 NA NAStep 3.6: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 11 extract.features.end 3 6 6 222.765 223.705
## 12 manage.missing.data 4 0 0 223.706 NA
## elapsed
## 11 0.94
## 12 NAStep 4.0: manage missing data
## [1] "numeric data missing in : "
## YOB Party.fctr
## 239 547
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024
## 2084 2065## [1] "numeric data missing in : "
## YOB Party.fctr
## 239 547
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024
## 2084 2065## label step_major step_minor label_minor bgn end
## 12 manage.missing.data 4 0 0 223.706 224.368
## 13 cluster.data 5 0 0 224.369 NA
## elapsed
## 12 0.662
## 13 NAStep 5.0: cluster data
```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## Loading required package: proxy##
## Attaching package: 'proxy'## The following objects are masked from 'package:stats':
##
## as.dist, dist## The following object is masked from 'package:base':
##
## as.matrix## Loading required package: dynamicTreeCut## Loading required package: entropy## Loading required package: tidyr## Loading required package: ggdendro## [1] "Clustering features: "## Warning in cor(data.matrix(glbObsAll[glbObsAll$.src == "Train",
## glbFeatsCluster]), : the standard deviation is zero## abs.cor.y
## Q113181.fctr 0.04357559
## Q102089.fctr 0.04804567
## Q100689.fctr 0.05185690
## Q113583.fctr 0.05306280
## Q101163.fctr 0.07163663
## [1] " .rnorm cor: 0.0268"
## [1] " Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6905"
## Hhold.fctr .clusterid Hhold.fctr.clusterid D R .entropy .knt
## 1 N 1 N_1 131 126 0.6929579 257
## 2 MKn 1 MKn_1 124 104 0.6892949 228
## 3 MKy 1 MKy_1 260 269 0.6930024 529
## 4 PKn 1 PKn_1 46 21 0.6218199 67
## 5 PKy 1 PKy_1 12 18 0.6730117 30
## 6 SKn 1 SKn_1 561 442 0.6860924 1003
## 7 SKy 1 SKy_1 37 33 0.6915136 70
## [1] "glbObsAll$Hhold.fctr Entropy: 0.6869 (99.4790 pct)"## [1] "Category: N"
## [1] "max distance(0.9804) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4666 5825 R N NA NA NA
## 6844 6410 <NA> N NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4666 NA NA NA NA NA
## 6844 NA Pc Yes No Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4666 NA NA NA NA NA
## 6844 Yes No Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4666 NA NA NA NA NA
## 6844 Science No Try first No Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4666 NA NA NA NA NA
## 6844 No Receiving Yes No Yes
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4666 NA NA NA NA NA
## 6844 Yes Id Yes Standard hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4666 NA NA NA NA NA
## 6844 No Happy No Yes No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4666 NA NA NA NA NA
## 6844 Yes A.M. No Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4666 NA NA NA NA NA
## 6844 Yes Cs Yes Yes Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4666 NA NA NA NA NA
## 6844 Yes No TMI Yes No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4666 NA NA No Yes Yes
## 6844 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4666 NA No Supportive Yes Mac
## 6844 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4666 NA NA NA NA NA
## 6844 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4666 NA NA NA NA No
## 6844 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4666 Yes Yes Gr Yes No
## 6844 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4666 No Yes Yes No Yes
## 6844 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4666 Yes NA NA NA NA
## 6844 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4666 NA Pessimist Dad Yes Yes
## 6844 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4666 Yes No Yes NA No
## 6844 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4666 Yes NA NA No No
## 6844 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4666 Yes NA No
## 6844 NA NA NA
## [1] "min distance(0.9658) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4230 5278 D N NA NA NA
## 4365 5451 R N NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA Odd hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA Yes NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4230 Yes NA NA NA NA
## 4365 NA NA NA Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4230 Space Yes NA NA NA
## 4365 Socialize Yes NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4230 NA Yes Gr NA Yes
## 4365 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4230 NA NA NA NA NA
## 4365 NA NA NA NA Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4230 No NA Yes No NA
## 4365 No Yes No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4230 Rent Pessimist Mom NA No
## 4365 Rent Optimist Mom No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4230 No Yes Yes NA No
## 4365 Yes Yes NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4230 No NA NA NA NA
## 4365 No Yes NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4230 NA NA NA
## 4365 NA NA NA
## [1] "Category: MKn"
## [1] "max distance(0.9806) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4905 6129 D MKn NA Yes NA
## 5820 1301 <NA> MKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4905 NA NA NA NA NA
## 5820 NA NA NA NA Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4905 NA NA Yes Yes Yes
## 5820 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4905 Science Yes Study first Yes No
## 5820 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4905 Yes Giving Yes Yes No
## 5820 NA NA Yes No NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4905 No Pr No Odd hours Cool headed
## 5820 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4905 No Right Yes Yes Yes
## 5820 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4905 No A.M. Yes Start Yes
## 5820 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4905 Yes Me Yes Yes No
## 5820 NA NA NA NA No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4905 Yes No TMI No No
## 5820 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4905 NA NA NA NA NA
## 5820 Talk Technology Yes Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4905 NA NA Demanding Yes NA
## 5820 Yes Yes Supportive NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4905 NA NA Cautious Yes! No
## 5820 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4905 Socialize No Online Yes Yes
## 5820 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4905 Yes Yes Yy NA NA
## 5820 NA NA NA Yes NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4905 NA NA NA NA NA
## 5820 NA Yes NA Yes NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4905 NA NA NA NA NA
## 5820 NA NA NA NA Yes
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4905 NA NA NA NA NA
## 5820 Rent NA NA Yes No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4905 NA NA NA NA NA
## 5820 NA Yes NA NA Yes
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4905 NA NA NA NA NA
## 5820 No Yes Yes NA Yes
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4905 NA NA NA
## 5820 Yes NA Yes
## [1] "min distance(0.9669) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4058 5057 D MKn NA NA NA
## 6396 4209 <NA> MKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4058 NA NA NA NA NA
## 6396 Yes NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4058 Talk Technology No No No
## 6396 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4058 Yes No Supportive No NA
## 6396 NA NA NA No NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4058 NA NA NA NA NA
## 6396 Yes NA Cautious NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4058 NA NA NA NA NA
## 6396 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4058 NA Optimist Mom NA No
## 6396 NA NA Mom NA No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4058 Yes Yes Yes Check! No
## 6396 No Yes Yes NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4058 No No Yes Yes No
## 6396 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4058 Only-child No No
## 6396 NA NA NA
## [1] "Category: MKy"
## [1] "max distance(0.9808) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1621 2008 D MKy NA NA NA
## 4029 5022 R MKy Yes Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1621 NA NA NA NA NA
## 4029 NA Pc No NA No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1621 NA NA NA NA NA
## 4029 NA NA NA Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1621 NA NA NA No No
## 4029 NA No Study first Yes NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1621 Yes Receiving No NA Yes
## 4029 NA Receiving NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1621 No Id No Standard hours Cool headed
## 4029 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1621 Yes Right Yes Yes No
## 4029 NA Happy NA NA No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1621 No P.M. No End Yes
## 4029 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1621 No Me NA NA NA
## 4029 NA NA NA Yes NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1621 NA NA NA NA NA
## 4029 NA NA NA NA Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1621 NA NA NA NA NA
## 4029 NA NA Yes NA Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1621 NA NA NA NA NA
## 4029 Yes NA NA NA Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1621 NA NA Risk-friendly Yes! No
## 4029 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1621 Socialize No Online Yes No
## 4029 NA Yes NA Yes NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1621 Yes Yes Yy Yes No
## 4029 NA Yes NA NA No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1621 Yes Yes Yes Yes No
## 4029 NA No NA No Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1621 Yes Yes NA NA NA
## 4029 NA NA NA Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1621 NA NA NA NA NA
## 4029 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1621 NA NA NA NA NA
## 4029 NA NA NA NA No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1621 NA NA NA NA No
## 4029 NA Yes NA No NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1621 Yes Yes Yes
## 4029 NA NA NA
## [1] "min distance(0.9660) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1257 1556 D MKy NA NA NA
## 4320 5395 D MKy NA NA Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1257 NA NA NA NA NA
## 4320 Yes NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA Yes Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA Yes NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1257 No NA No Standard hours NA
## 4320 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA No NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1257 Yes NA NA NA NA
## 4320 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA Yes
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA Yes Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1257 NA NA NA NA NA
## 4320 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1257 Rent Optimist Mom No Yes
## 4320 Rent Optimist Mom NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1257 NA Yes Yes Check! No
## 4320 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1257 No NA NA NA NA
## 4320 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1257 NA NA NA
## 4320 NA NA NA
## [1] "Category: PKn"
## [1] "max distance(0.9796) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3139 3912 R PKn NA Yes No
## 4323 5398 D PKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3139 No Pc No Yes No
## 4323 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3139 No Yes No Yes No
## 4323 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3139 Science Yes Study first No No
## 4323 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3139 No Giving No Yes NA
## 4323 NA NA NA Yes Yes
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3139 NA NA NA NA NA
## 4323 No Id No NA Hot headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3139 NA NA NA NA NA
## 4323 No Happy No Yes No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3139 NA NA Yes Start Yes
## 4323 No NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3139 Yes Me Yes No Yes
## 4323 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3139 No Yes TMI Yes Yes
## 4323 NA NA NA NA Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3139 NA NA NA NA NA
## 4323 Tunes Technology Yes Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3139 NA NA NA NA NA
## 4323 No Yes Supportive No NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3139 NA NA NA NA No
## 4323 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3139 NA No In-person No Yes
## 4323 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3139 Yes Yes Gr Yes Yes
## 4323 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3139 Yes No No Yes No
## 4323 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3139 NA NA NA NA NA
## 4323 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3139 NA NA NA NA NA
## 4323 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3139 NA NA NA NA NA
## 4323 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3139 NA NA NA NA NA
## 4323 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 3139 NA NA NA
## 4323 NA NA NA
## [1] "min distance(0.9690) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4738 5910 R PKn NA NA NA
## 6834 6356 <NA> PKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4738 No No NA No No
## 6834 No No NA NA Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4738 Tunes People No NA Yes
## 6834 Tunes Technology No NA Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4738 No Yes NA NA NA
## 6834 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4738 NA NA NA NA NA
## 6834 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4738 NA NA Yes
## 6834 NA NA NA
## [1] "No module detected"
## [1] "Category: PKy"
## [1] "max distance(0.9797) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1001 1244 R PKy Yes NA Yes
## 2346 2921 R PKy No No Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1001 No Pt NA NA NA
## 2346 No Pc No No Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1001 NA NA NA NA NA
## 2346 No Yes Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1001 Art NA Study first NA NA
## 2346 Science Yes Study first Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1001 NA NA No No NA
## 2346 Yes Giving Yes Yes No
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1001 NA NA NA NA NA
## 2346 Yes Id No Odd hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1001 NA Happy Yes Yes Yes
## 2346 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1001 NA A.M. Yes End Yes
## 2346 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1001 NA Me Yes Yes Yes
## 2346 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1001 Yes No Mysterious No No
## 2346 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1001 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1001 NA No Supportive No Mac
## 2346 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1001 NA NA NA Yes! No
## 2346 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1001 Socialize No Online No NA
## 2346 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1001 Yes NA NA NA NA
## 2346 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1001 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1001 NA Yes NA NA NA
## 2346 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1001 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1001 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1001 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1001 NA NA NA
## 2346 NA NA NA
## [1] "min distance(0.9701) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2749 3419 R PKy NA No Yes
## 4636 5786 D PKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2749 No NA NA NA Yes
## 4636 NA Pt Yes Yes Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2749 No Yes No Yes Yes
## 4636 No Yes NA No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2749 Art NA NA NA NA
## 4636 Science No Study first No No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2749 NA NA No No Yes
## 4636 No Giving No NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2749 No NA No Standard hours Cool headed
## 4636 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2749 No Right Yes No Yes
## 4636 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2749 Yes P.M. No Start Yes
## 4636 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2749 No Me No No Yes
## 4636 NA NA Yes No No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2749 No No TMI Yes No
## 4636 Yes Yes Mysterious No No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2749 Tunes Technology No Yes Yes
## 4636 Tunes Technology No Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2749 No No Demanding No NA
## 4636 No NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2749 Yes NA Cautious Umm... No
## 4636 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2749 Socialize No Online Yes No
## 4636 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2749 Yes No Gr No No
## 4636 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2749 Yes No Yes Yes No
## 4636 NA NA NA NA No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2749 Yes Yes No No No
## 4636 Yes Yes No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2749 Rent Pessimist Mom No Yes
## 4636 Rent Optimist Mom No NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2749 No Yes Yes Check! No
## 4636 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2749 Yes Yes No No No
## 4636 NA NA No No No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2749 Yes No No
## 4636 Yes No No
## [1] "No module detected"
## [1] "Category: SKn"
## [1] "max distance(0.9809) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 5090 6355 R SKn Yes Yes No
## 6751 5960 <NA> SKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 5090 No Pc Yes Yes No
## 6751 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 5090 Yes Yes Yes No Yes
## 6751 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 5090 Science No Study first No No
## 6751 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 5090 Yes Giving Yes Yes Yes
## 6751 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 5090 Yes Id Yes Standard hours Cool headed
## 6751 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 5090 Yes Happy Yes Yes No
## 6751 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 5090 Yes P.M. Yes End Yes
## 6751 NA NA NA Start NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 5090 No Me No Yes Yes
## 6751 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 5090 Yes No Mysterious NA NA
## 6751 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 5090 NA NA NA NA NA
## 6751 NA NA NA Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 5090 NA NA NA NA NA
## 6751 No No Supportive Yes PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 5090 NA NA NA NA NA
## 6751 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 5090 NA NA NA NA NA
## 6751 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 5090 NA NA NA NA NA
## 6751 NA NA NA Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 5090 NA NA NA NA NA
## 6751 Yes Yes No Yes Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 5090 NA NA NA NA NA
## 6751 Yes Yes No No No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 5090 NA NA NA NA NA
## 6751 Rent NA NA No No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 5090 NA NA NA NA NA
## 6751 No Yes Yes Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 5090 NA NA NA NA NA
## 6751 No No No Yes No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 5090 NA NA NA
## 6751 Yes Yes NA
## [1] "min distance(0.9648) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 5901 1696 <NA> SKn NA NA NA
## 6327 3843 <NA> SKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 5901 NA NA Demanding NA NA
## 6327 NA NA NA NA Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 5901 NA NA NA NA No
## 6327 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 5901 Socialize NA NA NA Yes
## 6327 NA NA NA NA No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 5901 NA NA NA NA NA
## 6327 No NA Gr NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 5901 NA NA NA NA NA
## 6327 NA No No No No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 5901 NA NA Mom NA NA
## 6327 NA Pessimist Mom NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 5901 Only-child NA NA
## 6327 NA NA NA
## [1] "Category: SKy"
## [1] "max distance(0.9807) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3522 4386 R SKy NA NA NA
## 6623 5332 <NA> SKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3522 NA NA NA NA Yes
## 6623 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3522 No Yes Yes Yes Yes
## 6623 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3522 Science Yes Study first No No
## 6623 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3522 No Giving No Yes Yes
## 6623 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3522 Yes Pr No Standard hours Hot headed
## 6623 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3522 Yes Happy Yes Yes Yes
## 6623 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3522 No P.M. NA NA NA
## 6623 NA NA Yes Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3522 NA NA NA NA NA
## 6623 No Me No NA No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3522 NA NA NA NA NA
## 6623 Yes No NA Yes NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3522 NA NA NA NA NA
## 6623 NA NA NA Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3522 NA NA NA NA NA
## 6623 No No Supportive Yes Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3522 NA NA NA NA NA
## 6623 Yes NA Cautious Umm... Yes
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3522 NA NA NA NA NA
## 6623 Space Yes NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3522 NA NA NA NA NA
## 6623 NA NA NA Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3522 NA NA NA NA NA
## 6623 Yes Yes NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3522 NA NA NA NA NA
## 6623 NA Yes Yes Yes Yes
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3522 Rent NA NA NA NA
## 6623 NA Optimist NA Yes No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3522 NA NA NA NA NA
## 6623 Yes Yes NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3522 Yes Yes No No No
## 6623 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 3522 Yes No NA
## 6623 NA NA NA
## [1] "min distance(0.9678) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3786 4714 D SKy NA Yes Yes
## 5803 1206 <NA> SKy No No NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3786 No NA NA NA NA
## 5803 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3786 NA NA No Yes No
## 5803 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3786 Art NA NA NA NA
## 5803 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA Supportive Yes Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3786 NA NA NA NA NA
## 5803 Yes NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3786 NA NA NA NA NA
## 5803 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3786 NA No Yes Yes Yes
## 5803 NA No No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3786 Rent Optimist Dad No Yes
## 5803 Own Pessimist Dad No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3786 Yes No NA NA NA
## 5803 No Yes NA NA No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3786 NA NA NA NA NA
## 5803 No Yes No No No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 3786 Yes NA No
## 5803 Yes No Yes
## Hhold.fctr .clusterid Hhold.fctr.clusterid D R .entropy .knt
## 1 N 1 N_1 40 40 0.6931472 80
## 2 N 2 N_2 30 29 0.6930035 59
## 3 N 3 N_3 27 21 0.6853142 48
## 4 N 4 N_4 13 25 0.6424220 38
## 5 N 5 N_5 21 11 0.6434916 32
## 6 MKn 1 MKn_1 30 42 0.6791933 72
## 7 MKn 2 MKn_2 32 21 0.6714519 53
## 8 MKn 3 MKn_3 23 14 0.6632647 37
## 9 MKn 4 MKn_4 21 14 0.6730117 35
## 10 MKn 5 MKn_5 18 13 0.6800829 31
## 11 MKy 1 MKy_1 114 128 0.6914729 242
## 12 MKy 2 MKy_2 99 83 0.6892779 182
## 13 MKy 3 MKy_3 47 58 0.6876496 105
## 14 PKn 1 PKn_1 19 9 0.6279416 28
## 15 PKn 2 PKn_2 9 4 0.6172418 13
## 16 PKn 3 PKn_3 6 4 0.6730117 10
## 17 PKn 4 PKn_4 5 3 0.6615632 8
## 18 PKn 5 PKn_5 7 1 0.3767702 8
## 19 PKy 1 PKy_1 2 5 0.5982696 7
## 20 PKy 2 PKy_2 3 5 0.6615632 8
## 21 PKy 3 PKy_3 3 3 0.6931472 6
## 22 PKy 4 PKy_4 2 4 0.6365142 6
## 23 PKy 5 PKy_5 2 1 0.6365142 3
## 24 SKn 1 SKn_1 264 193 0.6810296 457
## 25 SKn 2 SKn_2 91 83 0.6920899 174
## 26 SKn 3 SKn_3 76 59 0.6851974 135
## 27 SKn 4 SKn_4 74 53 0.6794132 127
## 28 SKn 5 SKn_5 56 54 0.6929819 110
## 29 SKy 1 SKy_1 15 12 0.6869616 27
## 30 SKy 2 SKy_2 10 10 0.6931472 20
## 31 SKy 3 SKy_3 3 8 0.5859526 11
## 32 SKy 4 SKy_4 9 3 0.5623351 12
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.6800 (98.9950 pct)"## label step_major step_minor label_minor bgn
## 13 cluster.data 5 0 0 224.369
## 14 partition.data.training 6 0 0 281.029
## end elapsed
## 13 281.028 56.659
## 14 NA NAStep 6.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.10 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.10 secs"
## [1] "lclgetMatrixSimilarity: duration: 19.537000 secs"## Loading required package: sampling##
## Attaching package: 'sampling'## The following object is masked from 'package:caret':
##
## cluster## Stratum 1
##
## Population total and number of selected units: 131 26
## Stratum 2
##
## Population total and number of selected units: 124 21
## Stratum 3
##
## Population total and number of selected units: 260 52
## Stratum 4
##
## Population total and number of selected units: 46 6
## Stratum 5
##
## Population total and number of selected units: 12 1
## Stratum 6
##
## Population total and number of selected units: 561 119
## Stratum 7
##
## Population total and number of selected units: 37 12
## Stratum 8
##
## Population total and number of selected units: 126 22
## Stratum 9
##
## Population total and number of selected units: 104 19
## Stratum 10
##
## Population total and number of selected units: 269 45
## Stratum 11
##
## Population total and number of selected units: 21 5
## Stratum 12
##
## Population total and number of selected units: 18 1
## Stratum 13
##
## Population total and number of selected units: 442 103
## Stratum 14
##
## Population total and number of selected units: 33 11
## Number of strata 14
## Total number of selected units 443
## [1] "lclgetMatrixSimilarity: duration: 11.991000 secs"
## [1] "lclgetMatrixSimilarity: duration: 4.123000 secs"
## [1] "lclgetMatrixSimilarity: duration: 3.900000 secs"
## [1] "lclgetMatrixSimilarity: duration: 8.880000 secs"
## [1] "Similarity of partitions:"
## cor cosineSmy obs.x obs.y
## 1 0.9999862 0.8859246 OOB Fit
## 2 0.9999863 0.9236707 OOB New
## 3 0.9999864 0.8285690 Fit New## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 50.24 secs"## Party.Democrat Party.Republican Party.NA
## NA NA 547
## Fit 934 807 NA
## OOB 237 206 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.5364733 0.4635267 NA
## OOB 0.5349887 0.4650113 NA
## Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6 SKn 781 222 276 0.44859276 0.501128668
## 2 MKy 432 97 121 0.24813326 0.218961625
## 3 N 209 48 59 0.12004595 0.108352144
## 1 MKn 188 40 49 0.10798392 0.090293454
## 7 SKy 47 23 28 0.02699598 0.051918736
## 4 PKn 56 11 12 0.03216542 0.024830700
## 5 PKy 28 2 2 0.01608271 0.004514673
## .freqRatio.Tst
## 6 0.504570384
## 2 0.221206581
## 3 0.107861060
## 1 0.089579525
## 7 0.051188300
## 4 0.021937843
## 5 0.003656307## [1] "glbObsAll: "## [1] 2731 222## [1] "glbObsTrn: "## [1] 2184 222## [1] "glbObsFit: "## [1] 1741 221## [1] "glbObsOOB: "## [1] 443 221## [1] "glbObsNew: "## [1] 547 221## [1] "partition.data.training chunk: teardown: elapsed: 50.87 secs"## label step_major step_minor label_minor bgn
## 14 partition.data.training 6 0 0 281.029
## 15 select.features 7 0 0 332.001
## end elapsed
## 14 332.001 50.972
## 15 NA NAStep 7.0: select features
```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## [1] "cor(Q98059.fctr, Q98078.fctr)=0.7770"
## [1] "cor(Party.fctr, Q98059.fctr)=-0.0411"
## [1] "cor(Party.fctr, Q98078.fctr)=-0.0435"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98059.fctr as highly correlated with Q98078.fctr## [1] "cor(Q100562.fctr, Q100680.fctr)=0.7680"
## [1] "cor(Party.fctr, Q100562.fctr)=-0.0339"
## [1] "cor(Party.fctr, Q100680.fctr)=-0.0273"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100562.fctr## [1] "cor(Q113583.fctr, Q113584.fctr)=0.7653"
## [1] "cor(Party.fctr, Q113583.fctr)=-0.0531"
## [1] "cor(Party.fctr, Q113584.fctr)=-0.0342"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q113584.fctr as highly correlated with Q113583.fctr## [1] "cor(Q102674.fctr, Q102687.fctr)=0.7442"
## [1] "cor(Party.fctr, Q102674.fctr)=-0.0418"
## [1] "cor(Party.fctr, Q102687.fctr)=-0.0306"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q102687.fctr as highly correlated with Q102674.fctr## [1] "cor(Q100562.fctr, Q100689.fctr)=0.7006"
## [1] "cor(Party.fctr, Q100562.fctr)=-0.0339"
## [1] "cor(Party.fctr, Q100689.fctr)=-0.0519"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100562.fctr as highly correlated with Q100689.fctr## cor.y exclude.as.feat cor.y.abs cor.high.X
## Gender.fctr 0.0909665772 0 0.0909665772 <NA>
## Q113181.fctr 0.0435755897 0 0.0435755897 <NA>
## .pos 0.0413144073 1 0.0413144073 <NA>
## USER_ID 0.0412669796 1 0.0412669796 <NA>
## Q120472.fctr 0.0373511418 0 0.0373511418 <NA>
## Q115611.fctr 0.0373054801 0 0.0373054801 <NA>
## Q120650.fctr 0.0358710311 0 0.0358710311 <NA>
## Q118237.fctr 0.0271389945 0 0.0271389945 <NA>
## .rnorm 0.0268489499 0 0.0268489499 <NA>
## Q122120.fctr 0.0234623979 0 0.0234623979 <NA>
## Q110740.fctr 0.0213122928 0 0.0213122928 <NA>
## Q122770.fctr 0.0203817957 0 0.0203817957 <NA>
## Q118117.fctr 0.0200889054 0 0.0200889054 <NA>
## Income.fctr 0.0178852251 0 0.0178852251 <NA>
## Q116441.fctr 0.0177162919 0 0.0177162919 <NA>
## Q118233.fctr 0.0176482250 0 0.0176482250 <NA>
## Q106272.fctr 0.0166660459 0 0.0166660459 <NA>
## Q119650.fctr 0.0154160890 0 0.0154160890 <NA>
## Q124742.fctr 0.0148384193 0 0.0148384193 <NA>
## Q122771.fctr 0.0146492330 0 0.0146492330 <NA>
## Q99480.fctr 0.0141594473 0 0.0141594473 <NA>
## Q116197.fctr 0.0130225570 0 0.0130225570 <NA>
## Q116881.fctr 0.0127923944 0 0.0127923944 <NA>
## Q101596.fctr 0.0122700322 0 0.0122700322 <NA>
## Q122769.fctr 0.0120730754 0 0.0120730754 <NA>
## Q108855.fctr 0.0116199609 0 0.0116199609 <NA>
## Q120014.fctr 0.0100200811 0 0.0100200811 <NA>
## Q119334.fctr 0.0097611771 0 0.0097611771 <NA>
## Q106993.fctr 0.0088906471 0 0.0088906471 <NA>
## Q107869.fctr 0.0084600631 0 0.0084600631 <NA>
## YOB 0.0065731919 1 0.0065731919 <NA>
## Q121011.fctr 0.0064795771 0 0.0064795771 <NA>
## Q117186.fctr 0.0061297032 0 0.0061297032 <NA>
## Q106997.fctr 0.0047472923 0 0.0047472923 <NA>
## YOB.Age.dff 0.0039888175 0 0.0039888175 <NA>
## Q108617.fctr 0.0034142713 0 0.0034142713 <NA>
## Q98197.fctr 0.0033385631 0 0.0033385631 <NA>
## Q106042.fctr 0.0028257871 0 0.0028257871 <NA>
## Q115777.fctr 0.0021874038 0 0.0021874038 <NA>
## Q123621.fctr 0.0020333068 0 0.0020333068 <NA>
## Q106388.fctr 0.0019532137 0 0.0019532137 <NA>
## Q114152.fctr -0.0002141693 0 0.0002141693 <NA>
## Q124122.fctr -0.0005523953 0 0.0005523953 <NA>
## Q120194.fctr -0.0008725662 0 0.0008725662 <NA>
## Q116797.fctr -0.0009782776 0 0.0009782776 <NA>
## Q105655.fctr -0.0019537389 0 0.0019537389 <NA>
## Q115899.fctr -0.0040294642 0 0.0040294642 <NA>
## Q116448.fctr -0.0042193065 0 0.0042193065 <NA>
## Q117193.fctr -0.0045436986 0 0.0045436986 <NA>
## Q108754.fctr -0.0052510790 0 0.0052510790 <NA>
## Q108856.fctr -0.0057486122 0 0.0057486122 <NA>
## YOB.Age.fctr -0.0071871098 0 0.0071871098 <NA>
## Q123464.fctr -0.0073497112 0 0.0073497112 <NA>
## Q99581.fctr -0.0075725773 0 0.0075725773 <NA>
## Q114961.fctr -0.0078051581 0 0.0078051581 <NA>
## Q104996.fctr -0.0087935260 0 0.0087935260 <NA>
## Q108343.fctr -0.0093294049 0 0.0093294049 <NA>
## Q120012.fctr -0.0094832005 0 0.0094832005 <NA>
## Q120978.fctr -0.0095190624 0 0.0095190624 <NA>
## Q98578.fctr -0.0127194176 0 0.0127194176 <NA>
## Q103293.fctr -0.0127467568 0 0.0127467568 <NA>
## Q106389.fctr -0.0127995068 0 0.0127995068 <NA>
## Q98869.fctr -0.0141131536 0 0.0141131536 <NA>
## Q112512.fctr -0.0148254430 0 0.0148254430 <NA>
## Q116953.fctr -0.0150205968 0 0.0150205968 <NA>
## Q100010.fctr -0.0157954167 0 0.0157954167 <NA>
## Q111220.fctr -0.0161563341 0 0.0161563341 <NA>
## Q102906.fctr -0.0162667502 0 0.0162667502 <NA>
## Q121700.fctr -0.0162998394 0 0.0162998394 <NA>
## Q112478.fctr -0.0164349791 0 0.0164349791 <NA>
## .clusterid -0.0164819548 1 0.0164819548 <NA>
## .clusterid.fctr -0.0164819548 0 0.0164819548 <NA>
## Q115610.fctr -0.0179375585 0 0.0179375585 <NA>
## Q119851.fctr -0.0188165770 0 0.0188165770 <NA>
## Q114517.fctr -0.0194814883 0 0.0194814883 <NA>
## Q118892.fctr -0.0197340603 0 0.0197340603 <NA>
## Q115602.fctr -0.0202866077 0 0.0202866077 <NA>
## Q120379.fctr -0.0203016988 0 0.0203016988 <NA>
## Q107491.fctr -0.0205240116 0 0.0205240116 <NA>
## Q114748.fctr -0.0209202111 0 0.0209202111 <NA>
## Q99982.fctr -0.0215133899 0 0.0215133899 <NA>
## Q113992.fctr -0.0222394292 0 0.0222394292 <NA>
## Q115390.fctr -0.0224688906 0 0.0224688906 <NA>
## Q118232.fctr -0.0257663213 0 0.0257663213 <NA>
## Q96024.fctr -0.0265018957 0 0.0265018957 <NA>
## Q115195.fctr -0.0271738479 0 0.0271738479 <NA>
## Q121699.fctr -0.0273324911 0 0.0273324911 <NA>
## Q100680.fctr -0.0273415528 0 0.0273415528 Q100562.fctr
## Q111580.fctr -0.0274150724 0 0.0274150724 <NA>
## Q102289.fctr -0.0285292574 0 0.0285292574 <NA>
## Q102687.fctr -0.0306196219 0 0.0306196219 Q102674.fctr
## Q105840.fctr -0.0307993280 0 0.0307993280 <NA>
## Q101162.fctr -0.0310084074 0 0.0310084074 <NA>
## Q108950.fctr -0.0317261524 0 0.0317261524 <NA>
## Q116601.fctr -0.0325709549 0 0.0325709549 <NA>
## Q108342.fctr -0.0332508344 0 0.0332508344 <NA>
## Q100562.fctr -0.0338636276 0 0.0338636276 Q100689.fctr
## Q113584.fctr -0.0341810079 0 0.0341810079 Q113583.fctr
## Q109367.fctr -0.0343630284 0 0.0343630284 <NA>
## Q99716.fctr -0.0374467543 0 0.0374467543 <NA>
## Hhold.fctr -0.0382423557 0 0.0382423557 <NA>
## Q112270.fctr -0.0396676511 0 0.0396676511 <NA>
## Q98059.fctr -0.0411225217 0 0.0411225217 Q98078.fctr
## Q111848.fctr -0.0412349958 0 0.0412349958 <NA>
## Q102674.fctr -0.0417938234 0 0.0417938234 <NA>
## Q114386.fctr -0.0423163811 0 0.0423163811 <NA>
## Q98078.fctr -0.0434942851 0 0.0434942851 <NA>
## Q102089.fctr -0.0480456671 0 0.0480456671 <NA>
## Edn.fctr -0.0493632201 0 0.0493632201 <NA>
## Q100689.fctr -0.0518568959 0 0.0518568959 <NA>
## Q113583.fctr -0.0530628021 0 0.0530628021 <NA>
## Q101163.fctr -0.0716366284 0 0.0716366284 <NA>
## Q109244.fctr NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Gender.fctr 1.450753 0.13736264 FALSE FALSE FALSE
## Q113181.fctr 3.968421 0.13736264 FALSE FALSE FALSE
## .pos 1.000000 100.00000000 FALSE FALSE FALSE
## USER_ID 1.000000 100.00000000 FALSE FALSE FALSE
## Q120472.fctr 1.896389 0.13736264 FALSE FALSE FALSE
## Q115611.fctr 2.705996 0.13736264 FALSE FALSE FALSE
## Q120650.fctr 1.112016 0.13736264 FALSE FALSE FALSE
## Q118237.fctr 3.284065 0.13736264 FALSE FALSE FALSE
## .rnorm 1.000000 100.00000000 FALSE FALSE FALSE
## Q122120.fctr 2.320883 0.13736264 FALSE FALSE TRUE
## Q110740.fctr 4.361345 0.13736264 FALSE FALSE TRUE
## Q122770.fctr 3.025974 0.13736264 FALSE FALSE TRUE
## Q118117.fctr 2.587649 0.13736264 FALSE FALSE TRUE
## Income.fctr 1.544160 0.32051282 FALSE FALSE TRUE
## Q116441.fctr 3.578824 0.13736264 FALSE FALSE TRUE
## Q118233.fctr 2.757576 0.13736264 FALSE FALSE TRUE
## Q106272.fctr 4.321149 0.13736264 FALSE FALSE TRUE
## Q119650.fctr 1.731915 0.13736264 FALSE FALSE TRUE
## Q124742.fctr 8.808612 0.13736264 FALSE FALSE TRUE
## Q122771.fctr 2.180534 0.13736264 FALSE FALSE TRUE
## Q99480.fctr 3.603139 0.13736264 FALSE FALSE TRUE
## Q116197.fctr 3.091858 0.13736264 FALSE FALSE TRUE
## Q116881.fctr 3.634259 0.13736264 FALSE FALSE TRUE
## Q101596.fctr 5.100304 0.13736264 FALSE FALSE TRUE
## Q122769.fctr 3.500000 0.13736264 FALSE FALSE TRUE
## Q108855.fctr 11.674847 0.13736264 FALSE FALSE TRUE
## Q120014.fctr 2.410681 0.13736264 FALSE FALSE TRUE
## Q119334.fctr 2.779193 0.13736264 FALSE FALSE TRUE
## Q106993.fctr 3.783599 0.13736264 FALSE FALSE TRUE
## Q107869.fctr 7.161826 0.13736264 FALSE FALSE TRUE
## YOB 1.091743 3.43406593 FALSE FALSE TRUE
## Q121011.fctr 2.049383 0.13736264 FALSE FALSE TRUE
## Q117186.fctr 3.433409 0.13736264 FALSE FALSE TRUE
## Q106997.fctr 6.438462 0.13736264 FALSE FALSE TRUE
## YOB.Age.dff 1.098214 0.86996337 FALSE FALSE TRUE
## Q108617.fctr 5.533742 0.13736264 FALSE FALSE TRUE
## Q98197.fctr 5.296178 0.13736264 FALSE FALSE TRUE
## Q106042.fctr 5.903915 0.13736264 FALSE FALSE TRUE
## Q115777.fctr 4.316667 0.13736264 FALSE FALSE TRUE
## Q123621.fctr 4.540059 0.13736264 FALSE FALSE TRUE
## Q106388.fctr 4.481283 0.13736264 FALSE FALSE TRUE
## Q114152.fctr 4.042929 0.13736264 FALSE FALSE TRUE
## Q124122.fctr 4.057292 0.13736264 FALSE FALSE TRUE
## Q120194.fctr 2.708163 0.13736264 FALSE FALSE TRUE
## Q116797.fctr 4.049738 0.13736264 FALSE FALSE TRUE
## Q105655.fctr 5.336634 0.13736264 FALSE FALSE TRUE
## Q115899.fctr 4.610619 0.13736264 FALSE FALSE TRUE
## Q116448.fctr 4.631420 0.13736264 FALSE FALSE TRUE
## Q117193.fctr 3.419355 0.13736264 FALSE FALSE TRUE
## Q108754.fctr 7.281746 0.13736264 FALSE FALSE TRUE
## Q108856.fctr 8.883721 0.13736264 FALSE FALSE TRUE
## YOB.Age.fctr 1.094828 0.41208791 FALSE FALSE TRUE
## Q123464.fctr 2.323988 0.13736264 FALSE FALSE TRUE
## Q99581.fctr 3.255578 0.13736264 FALSE FALSE TRUE
## Q114961.fctr 4.412791 0.13736264 FALSE FALSE TRUE
## Q104996.fctr 5.160256 0.13736264 FALSE FALSE TRUE
## Q108343.fctr 7.047244 0.13736264 FALSE FALSE TRUE
## Q120012.fctr 2.215613 0.13736264 FALSE FALSE TRUE
## Q120978.fctr 1.991394 0.13736264 FALSE FALSE TRUE
## Q98578.fctr 4.763689 0.13736264 FALSE FALSE TRUE
## Q103293.fctr 5.266234 0.13736264 FALSE FALSE TRUE
## Q106389.fctr 6.686275 0.13736264 FALSE FALSE TRUE
## Q98869.fctr 4.231552 0.13736264 FALSE FALSE TRUE
## Q112512.fctr 3.268994 0.13736264 FALSE FALSE TRUE
## Q116953.fctr 3.706444 0.13736264 FALSE FALSE TRUE
## Q100010.fctr 3.599558 0.13736264 FALSE FALSE TRUE
## Q111220.fctr 3.793269 0.13736264 FALSE FALSE TRUE
## Q102906.fctr 5.357827 0.13736264 FALSE FALSE TRUE
## Q121700.fctr 1.584833 0.13736264 FALSE FALSE TRUE
## Q112478.fctr 4.898507 0.13736264 FALSE FALSE TRUE
## .clusterid 1.793713 0.22893773 FALSE FALSE TRUE
## .clusterid.fctr 1.793713 0.22893773 FALSE FALSE TRUE
## Q115610.fctr 2.595819 0.13736264 FALSE FALSE TRUE
## Q119851.fctr 1.969543 0.13736264 FALSE FALSE TRUE
## Q114517.fctr 3.109244 0.13736264 FALSE FALSE TRUE
## Q118892.fctr 1.960591 0.13736264 FALSE FALSE TRUE
## Q115602.fctr 2.656420 0.13736264 FALSE FALSE TRUE
## Q120379.fctr 2.302326 0.13736264 FALSE FALSE TRUE
## Q107491.fctr 3.898383 0.13736264 FALSE FALSE TRUE
## Q114748.fctr 3.335664 0.13736264 FALSE FALSE TRUE
## Q99982.fctr 5.702703 0.13736264 FALSE FALSE TRUE
## Q113992.fctr 2.914172 0.13736264 FALSE FALSE TRUE
## Q115390.fctr 4.036176 0.13736264 FALSE FALSE TRUE
## Q118232.fctr 5.225490 0.13736264 FALSE FALSE TRUE
## Q96024.fctr 5.104938 0.13736264 FALSE FALSE TRUE
## Q115195.fctr 3.287912 0.13736264 FALSE FALSE FALSE
## Q121699.fctr 1.704385 0.13736264 FALSE FALSE FALSE
## Q100680.fctr 4.890533 0.13736264 FALSE FALSE FALSE
## Q111580.fctr 4.118863 0.13736264 FALSE FALSE FALSE
## Q102289.fctr 4.946429 0.13736264 FALSE FALSE FALSE
## Q102687.fctr 5.698246 0.13736264 FALSE FALSE FALSE
## Q105840.fctr 7.431034 0.13736264 FALSE FALSE FALSE
## Q101162.fctr 5.211838 0.13736264 FALSE FALSE FALSE
## Q108950.fctr 8.753488 0.13736264 FALSE FALSE FALSE
## Q116601.fctr 2.490694 0.13736264 FALSE FALSE FALSE
## Q108342.fctr 7.175299 0.13736264 FALSE FALSE FALSE
## Q100562.fctr 4.191919 0.13736264 FALSE FALSE FALSE
## Q113584.fctr 4.181058 0.13736264 FALSE FALSE FALSE
## Q109367.fctr 11.134503 0.13736264 FALSE FALSE FALSE
## Q99716.fctr 3.625821 0.13736264 FALSE FALSE FALSE
## Hhold.fctr 1.896030 0.32051282 FALSE FALSE FALSE
## Q112270.fctr 5.863799 0.13736264 FALSE FALSE FALSE
## Q98059.fctr 2.971805 0.13736264 FALSE FALSE FALSE
## Q111848.fctr 3.708229 0.13736264 FALSE FALSE FALSE
## Q102674.fctr 4.988166 0.13736264 FALSE FALSE FALSE
## Q114386.fctr 4.429395 0.13736264 FALSE FALSE FALSE
## Q98078.fctr 6.255556 0.13736264 FALSE FALSE FALSE
## Q102089.fctr 4.655271 0.13736264 FALSE FALSE FALSE
## Edn.fctr 1.103359 0.36630037 FALSE FALSE FALSE
## Q100689.fctr 4.553623 0.13736264 FALSE FALSE FALSE
## Q113583.fctr 3.138655 0.13736264 FALSE FALSE FALSE
## Q101163.fctr 6.202899 0.13736264 FALSE FALSE FALSE
## Q109244.fctr 0.000000 0.04578755 TRUE TRUE NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Q109244.fctr NA 0 NA <NA> 0
## percentUnique zeroVar nzv is.cor.y.abs.low
## Q109244.fctr 0.04578755 TRUE TRUE NA## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in : "
## YOB Party.fctr
## 239 547
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024 .lcn
## 2084 2065 547## [1] "glb_feats_df:"## [1] 113 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID 0.04126698 TRUE 0.04126698 <NA>
## Party.fctr Party.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID <NA> NA FALSE TRUE
## Party.fctr <NA> NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end
## 15 select.features 7 0 0 332.001 334.855
## 16 fit.models 8 0 0 334.855 NA
## elapsed
## 15 2.854
## 16 NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 335.572 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 335.572 335.606
## 2 fit.models_0_MFO 1 1 myMFO_classfr 335.607 NA
## elapsed
## 1 0.034
## 2 NA
## [1] "myfit_mdl: enter: 0.002000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.419000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
## D R
## 0.5364733 0.4635267
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.882000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used## [1] "myfit_mdl: train diagnostics complete: 0.885000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5364733 0.4635267
## 2 0.5364733 0.4635267
## 3 0.5364733 0.4635267
## 4 0.5364733 0.4635267
## 5 0.5364733 0.4635267
## 6 0.5364733 0.4635267
## Prediction
## Reference D R
## D 934 0
## R 807 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.364733e-01 0.000000e+00 5.127173e-01 5.601062e-01 5.364733e-01
## AccuracyPValue McnemarPValue
## 5.098183e-01 4.412715e-177
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5364733 0.4635267
## 2 0.5364733 0.4635267
## 3 0.5364733 0.4635267
## 4 0.5364733 0.4635267
## 5 0.5364733 0.4635267
## 6 0.5364733 0.4635267## Prediction
## Reference D R
## D 237 0
## R 206 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.349887e-01 0.000000e+00 4.873124e-01 5.821948e-01 5.349887e-01
## AccuracyPValue McnemarPValue
## 5.194323e-01 2.792063e-46
## [1] "myfit_mdl: predict complete: 6.389000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.455
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.003 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5364733
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5127173 0.5601062 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5349887
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4873124 0.5821948 0
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5364733 0.4635267
## 2 0.5364733 0.4635267
## 3 0.5364733 0.4635267
## 4 0.5364733 0.4635267
## 5 0.5364733 0.4635267
## 6 0.5364733 0.4635267
## [1] "myfit_mdl: exit: 6.436000 secs"
## label step_major step_minor label_minor bgn
## 2 fit.models_0_MFO 1 1 myMFO_classfr 335.607
## 3 fit.models_0_Random 1 2 myrandom_classfr 342.049
## end elapsed
## 2 342.048 6.441
## 3 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.417000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.723000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.725000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 934 0
## R 807 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.364733e-01 0.000000e+00 5.127173e-01 5.601062e-01 5.364733e-01
## AccuracyPValue McnemarPValue
## 5.098183e-01 4.412715e-177
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 237 0
## R 206 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.349887e-01 0.000000e+00 4.873124e-01 5.821948e-01 5.349887e-01
## AccuracyPValue McnemarPValue
## 5.194323e-01 2.792063e-46
## [1] "myfit_mdl: predict complete: 6.838000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.301 0.002 0.483755
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5214133 0.4460967 0.5049102 0.55
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.5364733 0.5127173
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5601062 0 0.5555897 0.6160338
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.4951456 0.5054791 0.55 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5349887 0.4873124 0.5821948
## max.Kappa.OOB
## 1 0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 7.571000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 342.049 349.633 7.584
## 4 349.634 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Gender.fctr,Q101163.fctr"
## [1] "myfit_mdl: setup complete: 0.685000 secs"## Loading required package: glmnet## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.00104 on full training set
## [1] "myfit_mdl: train complete: 1.488000 secs"
## alpha lambda
## 1 0.1 0.00104043
## Length Class Mode
## a0 46 -none- numeric
## beta 184 dgCMatrix S4
## df 46 -none- numeric
## dim 2 -none- numeric
## lambda 46 -none- numeric
## dev.ratio 46 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Q101163.fctrDad
## -0.29183047 -0.02826816 0.34103527 0.16410896
## Q101163.fctrMom
## -0.67736154
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Gender.fctrF" "Gender.fctrM" "Q101163.fctrDad"
## [5] "Q101163.fctrMom"
## [1] "myfit_mdl: train diagnostics complete: 1.597000 secs"
## Prediction
## Reference D R
## D 495 439
## R 329 478
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.588742e-01 1.211729e-01 5.351811e-01 5.823686e-01 5.364733e-01
## AccuracyPValue McnemarPValue
## 3.201504e-02 8.382289e-05
## Prediction
## Reference D R
## D 218 19
## R 181 25
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.485327e-01 4.342381e-02 5.008705e-01 5.955427e-01 5.349887e-01
## AccuracyPValue McnemarPValue
## 3.004764e-01 5.000028e-30
## [1] "myfit_mdl: predict complete: 7.259000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Gender.fctr,Q101163.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.796 0.027 0.5611479
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5299786 0.5923172 0.5742002 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.5545244 0.5588742 0.5351811
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5823686 0.1211729 0.5471714 0.5021097
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.592233 0.5505203 0.55 0.2
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5485327 0.5008705 0.5955427
## max.Kappa.OOB
## 1 0.04342381
## [1] "myfit_mdl: exit: 7.325000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Gender.fctr,Q101163.fctr"
## [1] "myfit_mdl: setup complete: 0.693000 secs"## Loading required package: rpart## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00604 on full training set
## [1] "myfit_mdl: train complete: 2.102000 secs"## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1741
##
## CP nsplit rel error
## 1 0.02416357 0 1.0000000
## 2 0.00000000 2 0.9516729
##
## Variable importance
## Q101163.fctrMom Gender.fctrM Gender.fctrF
## 40 31 29
##
## Node number 1: 1741 observations, complexity param=0.02416357
## predicted class=D expected loss=0.4635267 P(node) =1
## class counts: 934 807
## probabilities: 0.536 0.464
## left son=2 (160 obs) right son=3 (1581 obs)
## Primary splits:
## Q101163.fctrMom < 0.5 to the right, improve=9.423110, (0 missing)
## Gender.fctrM < 0.5 to the left, improve=8.554592, (0 missing)
## Gender.fctrF < 0.5 to the right, improve=7.376294, (0 missing)
## Q101163.fctrDad < 0.5 to the left, improve=1.465965, (0 missing)
##
## Node number 2: 160 observations
## predicted class=D expected loss=0.3 P(node) =0.09190121
## class counts: 112 48
## probabilities: 0.700 0.300
##
## Node number 3: 1581 observations, complexity param=0.02416357
## predicted class=D expected loss=0.4800759 P(node) =0.9080988
## class counts: 822 759
## probabilities: 0.520 0.480
## left son=6 (664 obs) right son=7 (917 obs)
## Primary splits:
## Gender.fctrM < 0.5 to the left, improve=7.408454, (0 missing)
## Gender.fctrF < 0.5 to the right, improve=6.635045, (0 missing)
## Q101163.fctrDad < 0.5 to the left, improve=0.734389, (0 missing)
## Surrogate splits:
## Gender.fctrF < 0.5 to the right, agree=0.968, adj=0.923, (0 split)
##
## Node number 6: 664 observations
## predicted class=D expected loss=0.4231928 P(node) =0.38139
## class counts: 383 281
## probabilities: 0.577 0.423
##
## Node number 7: 917 observations
## predicted class=R expected loss=0.478735 P(node) =0.5267088
## class counts: 439 478
## probabilities: 0.479 0.521
##
## n= 1741
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1741 807 D (0.5364733 0.4635267)
## 2) Q101163.fctrMom>=0.5 160 48 D (0.7000000 0.3000000) *
## 3) Q101163.fctrMom< 0.5 1581 759 D (0.5199241 0.4800759)
## 6) Gender.fctrM< 0.5 664 281 D (0.5768072 0.4231928) *
## 7) Gender.fctrM>=0.5 917 439 R (0.4787350 0.5212650) *
## [1] "myfit_mdl: train diagnostics complete: 2.964000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.5545244 0.5588742
## 11 0.50 0.5545244 0.5588742
## Prediction
## Reference D R
## D 495 439
## R 329 478
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.588742e-01 1.211729e-01 5.351811e-01 5.823686e-01 5.364733e-01
## AccuracyPValue McnemarPValue
## 3.201504e-02 8.382289e-05
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.5470852 0.5440181
## 11 0.50 0.5470852 0.5440181
## Prediction
## Reference D R
## D 119 118
## R 84 122
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.54401806 0.09333522 0.49634736 0.59109720 0.53498871
## AccuracyPValue McnemarPValue
## 0.36981158 0.02023983
## [1] "myfit_mdl: predict complete: 8.837000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Gender.fctr,Q101163.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.403 0.012 0.5611479
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5299786 0.5923172 0.56983 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.5545244 0.558868 0.5351811
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5823686 0.1211457 0.5471714 0.5021097
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.592233 0.5511143 0.5 0.5470852
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5440181 0.4963474 0.5910972
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.09333522 0.01891925 0.03781364
## [1] "myfit_mdl: exit: 8.914000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## bgn end elapsed
## 4 349.634 365.915 16.281
## 5 365.916 NA NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q101163.fctr,Gender.fctr:Q100562.fctr,Gender.fctr:Q102674.fctr,Gender.fctr:Q100689.fctr,Gender.fctr:Q113583.fctr,Gender.fctr:Q98078.fctr"
## [1] "myfit_mdl: setup complete: 0.725000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.00483 on full training set
## [1] "myfit_mdl: train complete: 3.433000 secs"
## Length Class Mode
## a0 64 -none- numeric
## beta 2176 dgCMatrix S4
## df 64 -none- numeric
## dim 2 -none- numeric
## lambda 64 -none- numeric
## dev.ratio 64 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 34 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrM
## -0.296116702 0.384875316
## Q101163.fctrDad Q101163.fctrMom
## 0.239136757 -0.573952050
## Gender.fctrF:Q100562.fctrNo Gender.fctrM:Q100562.fctrNo
## 0.757657100 0.405282922
## Gender.fctrN:Q100562.fctrYes Gender.fctrF:Q100562.fctrYes
## -1.388511415 0.369754473
## Gender.fctrM:Q100562.fctrYes Gender.fctrN:Q100689.fctrNo
## -0.185322539 -1.497720041
## Gender.fctrF:Q100689.fctrNo Gender.fctrN:Q100689.fctrYes
## -0.223231972 1.729952318
## Gender.fctrF:Q100689.fctrYes Gender.fctrM:Q100689.fctrYes
## -0.223549442 -0.224211153
## Gender.fctrN:Q102674.fctrNo Gender.fctrF:Q102674.fctrNo
## 0.669008565 -0.023769150
## Gender.fctrM:Q102674.fctrNo Gender.fctrF:Q102674.fctrYes
## 0.221925496 -0.731761825
## Gender.fctrM:Q102674.fctrYes Gender.fctrN:Q113583.fctrTalk
## -0.007445843 2.753045773
## Gender.fctrF:Q113583.fctrTalk Gender.fctrM:Q113583.fctrTalk
## -0.180685129 0.177823281
## Gender.fctrN:Q113583.fctrTunes Gender.fctrF:Q113583.fctrTunes
## 0.384692060 -0.097491545
## Gender.fctrM:Q113583.fctrTunes Gender.fctrN:Q98078.fctrNo
## -0.267589962 -1.170188735
## Gender.fctrF:Q98078.fctrNo Gender.fctrM:Q98078.fctrNo
## -0.034329453 -0.172824227
## Gender.fctrN:Q98078.fctrYes Gender.fctrF:Q98078.fctrYes
## 0.202015017 -0.127696392
## Gender.fctrM:Q98078.fctrYes
## -0.099161250
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrM
## -0.296198099 0.386598546
## Q101163.fctrDad Q101163.fctrMom
## 0.243672991 -0.573977658
## Gender.fctrF:Q100562.fctrNo Gender.fctrM:Q100562.fctrNo
## 0.784779498 0.409295833
## Gender.fctrN:Q100562.fctrYes Gender.fctrF:Q100562.fctrYes
## -1.511831394 0.393405406
## Gender.fctrM:Q100562.fctrYes Gender.fctrN:Q100689.fctrNo
## -0.186732556 -1.566905404
## Gender.fctrF:Q100689.fctrNo Gender.fctrM:Q100689.fctrNo
## -0.242312964 -0.001548176
## Gender.fctrN:Q100689.fctrYes Gender.fctrF:Q100689.fctrYes
## 1.857025887 -0.240616162
## Gender.fctrM:Q100689.fctrYes Gender.fctrN:Q102674.fctrNo
## -0.229526327 0.719021643
## Gender.fctrF:Q102674.fctrNo Gender.fctrM:Q102674.fctrNo
## -0.028539659 0.226248904
## Gender.fctrF:Q102674.fctrYes Gender.fctrM:Q102674.fctrYes
## -0.739945024 -0.007752804
## Gender.fctrN:Q113583.fctrTalk Gender.fctrF:Q113583.fctrTalk
## 2.849206894 -0.185642064
## Gender.fctrM:Q113583.fctrTalk Gender.fctrN:Q113583.fctrTunes
## 0.179544299 0.382807827
## Gender.fctrF:Q113583.fctrTunes Gender.fctrM:Q113583.fctrTunes
## -0.099021903 -0.270480111
## Gender.fctrN:Q98078.fctrNo Gender.fctrF:Q98078.fctrNo
## -1.201651743 -0.039508747
## Gender.fctrM:Q98078.fctrNo Gender.fctrN:Q98078.fctrYes
## -0.177336610 0.218714021
## Gender.fctrF:Q98078.fctrYes Gender.fctrM:Q98078.fctrYes
## -0.132997741 -0.103779257
## [1] "myfit_mdl: train diagnostics complete: 4.112000 secs"
## Prediction
## Reference D R
## D 588 346
## R 387 420
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5789775991 0.1505163190 0.5553817730 0.6023067691 0.5364732912
## AccuracyPValue McnemarPValue
## 0.0001991788 0.1395594155
## Prediction
## Reference D R
## D 152 85
## R 104 102
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.57336343 0.13732420 0.52581675 0.61992398 0.53498871
## AccuracyPValue McnemarPValue
## 0.05776524 0.19043026
## [1] "myfit_mdl: predict complete: 10.146000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 Gender.fctr,Q101163.fctr,Gender.fctr:Q100562.fctr,Gender.fctr:Q102674.fctr,Gender.fctr:Q100689.fctr,Gender.fctr:Q113583.fctr,Gender.fctr:Q98078.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.698 0.114
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5749982 0.6295503 0.5204461 0.6082637
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5340114 0.5540889
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5553818 0.6023068 0.1004791
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5682479 0.6413502 0.4951456 0.5847057
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.519084 0.5733634
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5258168 0.619924 0.1373242
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01751427 0.03444344
## [1] "myfit_mdl: exit: 10.237000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## bgn end elapsed
## 5 365.916 376.17 10.254
## 6 376.170 NA NAindepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q114386.fctr,Q102089.fctr,Edn.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.690000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.025 on full training set
## [1] "myfit_mdl: train complete: 8.849000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 66 -none- numeric
## beta 16962 dgCMatrix S4
## df 66 -none- numeric
## dim 2 -none- numeric
## lambda 66 -none- numeric
## dev.ratio 66 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 257 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.24856701 -0.01825592
## Edn.fctr^4 Gender.fctrM
## 0.07665110 0.18955032
## Hhold.fctrPKn Q101163.fctrMom
## -0.12291763 -0.36793581
## Q106389.fctrNo Q108950.fctrRisk-friendly
## 0.06086792 -0.09022225
## Q109367.fctrYes Q111848.fctrYes
## -0.05743248 -0.07642739
## Q113181.fctrYes Q114386.fctrTMI
## 0.40453966 -0.09999585
## Q115611.fctrNo Q115611.fctrYes
## -0.16296329 0.33579493
## Q116441.fctrNo Q116441.fctrYes
## -0.06532856 0.08177310
## Q116601.fctrNo Q119851.fctrNo
## 0.03737031 0.11382449
## Q120379.fctrNo Q120379.fctrYes
## 0.05079844 -0.01759372
## Q120650.fctrNo Q98197.fctrNo
## -0.11841144 -0.17031844
## Q98869.fctrNo YOB.Age.fctr^8
## -0.08319400 0.05473263
## Hhold.fctrN:.clusterid.fctr4
## 0.13636242
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.254809720 -0.030292261
## Edn.fctr^4 Edn.fctr^7
## 0.090562354 -0.009556109
## Gender.fctrM Hhold.fctrPKn
## 0.201605249 -0.151465263
## Hhold.fctrPKy Q101163.fctrMom
## 0.051095847 -0.391738029
## Q106389.fctrNo Q108950.fctrRisk-friendly
## 0.097899763 -0.127445461
## Q109367.fctrYes Q111848.fctrYes
## -0.088886066 -0.093960249
## Q113181.fctrYes Q114386.fctrTMI
## 0.431607566 -0.122732213
## Q115611.fctrNo Q115611.fctrYes
## -0.163786252 0.356695958
## Q115899.fctrCs Q116441.fctrNo
## -0.014762123 -0.080866559
## Q116441.fctrYes Q116601.fctrNo
## 0.095518086 0.072326701
## Q119851.fctrNo Q120012.fctrNo
## 0.130530450 0.011016916
## Q120379.fctrNo Q120379.fctrYes
## 0.048212945 -0.038918039
## Q120472.fctrScience Q120650.fctrNo
## 0.007834446 -0.151433426
## Q122771.fctrPt Q98197.fctrNo
## 0.019838964 -0.185300085
## Q98869.fctrNo Q99480.fctrYes
## -0.106502355 0.010079056
## YOB.Age.fctr^8 Hhold.fctrN:.clusterid.fctr4
## 0.082089959 0.184580610
## Hhold.fctrSKy:.clusterid.fctr4
## -0.010609690
## [1] "myfit_mdl: train diagnostics complete: 9.559000 secs"
## Prediction
## Reference D R
## D 495 439
## R 239 568
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.105686e-01 2.299455e-01 5.872056e-01 6.335582e-01 5.364733e-01
## AccuracyPValue McnemarPValue
## 2.665901e-10 2.129630e-14
## Prediction
## Reference D R
## D 111 126
## R 60 146
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.801354e-01 1.734577e-01 5.326406e-01 6.265530e-01 5.349887e-01
## AccuracyPValue McnemarPValue
## 3.136438e-02 1.878901e-06
## [1] "myfit_mdl: predict complete: 18.739000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q114386.fctr,Q102089.fctr,Edn.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 8.077 0.631
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5904459 0.8190578 0.361834 0.6668517
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.45 0.6262404 0.5764843
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5872056 0.6335582 0.1267896
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5447954 0.7594937 0.3300971 0.606325
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.45 0.6108787 0.5801354
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5326406 0.626553 0.1734577
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01749145 0.03668815
## [1] "myfit_mdl: exit: 19.022000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 376.170 395.233
## 7 fit.models_0_end 1 6 teardown 395.234 NA
## elapsed
## 6 19.063
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 0 0 334.855 395.249 60.395
## 17 fit.models 8 1 1 395.250 NA NA```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 400.289 NA NA## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 400.289 400.302
## 2 fit.models_1_All.X 1 1 setup 400.303 NA
## elapsed
## 1 0.013
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 400.303 400.311
## 3 fit.models_1_All.X 1 2 glmnet 400.311 NA
## elapsed
## 2 0.008
## 3 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.715000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.025 on full training set
## [1] "myfit_mdl: train complete: 10.150000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 66 -none- numeric
## beta 17622 dgCMatrix S4
## df 66 -none- numeric
## dim 2 -none- numeric
## lambda 66 -none- numeric
## dev.ratio 66 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 267 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.24633071 -0.01740549
## Edn.fctr^4 Gender.fctrM
## 0.07520599 0.18732069
## Hhold.fctrPKn Q100562.fctrNo
## -0.12110652 0.04313257
## Q101163.fctrMom Q102674.fctrYes
## -0.36238096 -0.08862783
## Q106389.fctrNo Q108950.fctrRisk-friendly
## 0.06215632 -0.08896436
## Q109367.fctrYes Q111848.fctrYes
## -0.05341932 -0.06772586
## Q113181.fctrYes Q113583.fctrTunes
## 0.40792456 -0.02284092
## Q114386.fctrTMI Q115611.fctrNo
## -0.09555340 -0.16037163
## Q115611.fctrYes Q116441.fctrNo
## 0.33873395 -0.06557350
## Q116441.fctrYes Q116601.fctrNo
## 0.09057368 0.03483364
## Q119851.fctrNo Q120379.fctrNo
## 0.11360341 0.05302166
## Q120379.fctrYes Q120650.fctrNo
## -0.01506287 -0.11989437
## Q98197.fctrNo Q98869.fctrNo
## -0.16546422 -0.08143157
## YOB.Age.fctr^8 Hhold.fctrN:.clusterid.fctr4
## 0.05402258 0.13343316
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.251661553 -0.029652441
## Edn.fctr^4 Edn.fctr^7
## 0.088012057 -0.010038296
## Gender.fctrM Hhold.fctrPKn
## 0.198708881 -0.148849865
## Hhold.fctrPKy Q100562.fctrNo
## 0.048154800 0.074016756
## Q101163.fctrMom Q101596.fctrYes
## -0.387635203 0.002693313
## Q102674.fctrYes Q106389.fctrNo
## -0.119440274 0.096390006
## Q108950.fctrRisk-friendly Q109367.fctrYes
## -0.125504143 -0.083765949
## Q111848.fctrYes Q113181.fctrYes
## -0.081825884 0.435899680
## Q113583.fctrTunes Q114386.fctrTMI
## -0.037510699 -0.116421093
## Q115611.fctrNo Q115611.fctrYes
## -0.159119674 0.359777700
## Q115899.fctrCs Q116441.fctrNo
## -0.016176456 -0.081489290
## Q116441.fctrYes Q116601.fctrNo
## 0.106122378 0.070002273
## Q119851.fctrNo Q120012.fctrNo
## 0.130494940 0.010313790
## Q120379.fctrNo Q120379.fctrYes
## 0.052638428 -0.033521781
## Q120472.fctrScience Q120650.fctrNo
## 0.003987252 -0.152898742
## Q122771.fctrPt Q98197.fctrNo
## 0.018750434 -0.181192288
## Q98869.fctrNo Q99480.fctrYes
## -0.106090726 0.017688107
## YOB.Age.fctr^8 Hhold.fctrN:.clusterid.fctr4
## 0.081116701 0.180407402
## [1] "myfit_mdl: train diagnostics complete: 10.828000 secs"
## Prediction
## Reference D R
## D 773 161
## R 507 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.163125e-01 2.053563e-01 5.930025e-01 6.392296e-01 5.364733e-01
## AccuracyPValue McnemarPValue
## 1.069346e-11 1.209380e-40
## Prediction
## Reference D R
## D 115 122
## R 56 150
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.981941e-01 2.090079e-01 5.508809e-01 6.441871e-01 5.349887e-01
## AccuracyPValue McnemarPValue
## 4.280853e-03 1.104988e-06
## [1] "myfit_mdl: predict complete: 20.270000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 9.352 0.75
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5996852 0.8276231 0.3717472 0.6711596
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.4731861 0.5764853
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5930025 0.6392296 0.1273592
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.551442 0.7679325 0.3349515 0.6177236
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.45 0.6276151 0.5981941
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5508809 0.6441871 0.2090079
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01604823 0.03327071
## [1] "myfit_mdl: exit: 20.555000 secs"## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 400.311 420.892
## 4 fit.models_1_preProc 1 3 preProc 420.892 NA
## elapsed
## 3 20.581
## 4 NA## min.elapsedtime.everything
## Random###myrandom_classfr 0.301
## MFO###myMFO_classfr 0.455
## Max.cor.Y.rcv.1X1###glmnet 0.796
## Max.cor.Y##rcv#rpart 1.403
## Interact.High.cor.Y##rcv#glmnet 2.698
## Low.cor.X##rcv#glmnet 8.077
## All.X##rcv#glmnet 9.352## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 420.892 420.939
## 5 fit.models_1_end 1 4 teardown 420.940 NA
## elapsed
## 4 0.047
## 5 NA## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 1 1 395.25 420.949 25.699
## 18 fit.models 8 2 2 420.95 NA NA```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)Step 8.2: fit models
```{r fit.data.training_0, cache=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification)
# mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
# mdlEnsembleComps <- gsub(paste0("^",
# gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
# "", mdlEnsembleComps)
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlEnsembleComps)] else
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlEnsembleComps)]
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
# glb_fin_mdl uses the same coefficients as glb_sel_mdl,
# so copy the "Final" columns into "non-Final" columns
glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))
print(setdiff(names(glbObsOOB), names(glbObsAll)))
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)Step 8.2: fit models
Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 2 inspect.data 2 0 0 19.975
## 16 fit.models 8 0 0 334.855
## 13 cluster.data 5 0 0 224.369
## 14 partition.data.training 6 0 0 281.029
## 3 scrub.data 2 1 1 176.309
## 17 fit.models 8 1 1 395.250
## 1 import.data 1 0 0 7.843
## 15 select.features 7 0 0 332.001
## 11 extract.features.end 3 6 6 222.765
## 12 manage.missing.data 4 0 0 223.706
## 10 extract.features.string 3 5 5 222.695
## 9 extract.features.text 3 4 4 222.633
## 7 extract.features.image 3 2 2 222.539
## 4 transform.data 2 2 2 222.430
## 6 extract.features.datetime 3 1 1 222.496
## 8 extract.features.price 3 3 3 222.595
## 5 extract.features 3 0 0 222.474
## end elapsed duration
## 2 176.308 156.333 156.333
## 16 395.249 60.395 60.394
## 13 281.028 56.659 56.659
## 14 332.001 50.972 50.972
## 3 222.429 46.121 46.120
## 17 420.949 25.699 25.699
## 1 19.975 12.132 12.132
## 15 334.855 2.854 2.854
## 11 223.705 0.940 0.940
## 12 224.368 0.662 0.662
## 10 222.765 0.070 0.070
## 9 222.695 0.062 0.062
## 7 222.595 0.056 0.056
## 4 222.473 0.044 0.043
## 6 222.538 0.042 0.042
## 8 222.632 0.037 0.037
## 5 222.495 0.021 0.021
## [1] "Total Elapsed Time: 420.949 secs"